Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to natural language processing, more specifically, relates to contextual sarcasm identification system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] Enabling a more accurate understanding of sarcasm in different communication styles, allowing for better interpretation of messages across various contexts. By recognizing sarcastic expressions more effectively, misunderstandings in digital and social interactions are significantly reduced. This leads to improved engagement in conversations, ensuring that responses are more relevant and appropriate. With a refined ability to detect sarcasm, users benefit from clearer communication, fostering stronger personal and professional relationships. The system enhances content moderation and online discussions by filtering out misinterpreted sarcasm that could lead to unnecessary disputes.
[0003] Providing adaptability to different linguistic and cultural variations, allowing for a broader and more inclusive understanding of sarcasm across diverse populations. Many expressions differ in meaning depending on regional and cultural influences, which this system effectively interprets. This inclusivity ensures that sarcasm is detected with higher consistency, regardless of language nuances and user demographics. The system creates a more effective bridge between varied communication styles, helping individuals from different backgrounds engage in conversations without confusion. By accounting for different ways sarcasm is conveyed, digital platforms, customer support services, and automated systems can respond more accurately to user interactions.
[0004] Ensuring reliability in sarcasm detection, even when faced with challenging sentence structures and ambiguous statements. Many sarcastic expressions are subtle and require a deeper understanding of context, which this system effectively addresses. By overcoming common issues related to misinterpretation, users experience more seamless interactions, reducing frustration caused by inaccurate responses. The system enhances user experience in virtual conversations, making automated assistants, sentiment analysis tools, and social media platforms more effective in understanding human emotions. This reliability ensures that sarcasm detection remains efficient even in fast-paced digital conversations where clarity is essential.
[0005] Similar existing inventions are struggling to accurately identify sarcasm when expressions lack clear indicators, leading to frequent misinterpretations. Many systems rely on basic word patterns without considering deeper meanings, causing errors in sarcasm detection. This limitation results in responses that do not align with the actual intent of the conversation, reducing the effectiveness of digital interactions. The lack of contextual understanding causes existing solutions to frequently misclassify neutral statements as sarcastic or fail to recognize sarcasm when intended. This inconsistency affects the overall user experience and diminishes trust in automated systems.
[0006] Similar existing inventions are lacking adaptability to different languages and dialects, limiting their effectiveness across diverse populations. Many sarcasm detection tools fail to recognize expressions unique to specific regions, leading to incorrect interpretations. Users from different linguistic backgrounds experience reduced accuracy in sarcasm detection, affecting global communication and engagement. The failure to adapt to variations in language results in a less effective system, preventing seamless cross-cultural understanding. This limitation restricts the ability of current sarcasm detection systems to function effectively in multilingual and multicultural settings.
[0007] Many tools struggle to differentiate between intentional sarcasm and deceptive language designed to manipulate detection models. This flaw makes existing systems susceptible to exploitation, leading to incorrect classification of sarcastic and non-sarcastic expressions. The inability to handle adversarial inputs weakens the robustness of sarcasm detection tools, affecting their practical application in real-world scenarios. Users relying on these tools for accurate sentiment analysis experience frequent errors, impacting decision-making and automated communication processes.
[0008] Thus, in light of the above-stated discussion, there exists a need for a contextual sarcasm identification system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on a contextual sarcasm identification system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to provide a system for accurately identifying sarcasm across various communication styles, ensuring that both direct and implied sarcastic expressions are effectively recognized without misinterpretation.
[0012] Another objective of the present disclosure is to enhance contextual understanding by incorporating linguistic and cultural variations, allowing sarcasm detection to remain consistent and reliable across different demographic groups and social settings.
[0013] Another objective of the present disclosure is to reduce miscommunication in digital conversations by improving sarcasm detection accuracy, enabling clearer and more effective exchanges between users in professional and social interactions.
[0014] Another objective of the present disclosure is to improve sentiment analysis by distinguishing between sarcastic and non-sarcastic expressions, allowing for more precise insights in fields such as social media monitoring, customer feedback analysis, and automated communication systems.
[0015] Another objective of the present disclosure is to strengthen engagement in digital platforms by ensuring that sarcasm detection does not interfere with natural language flow while still identifying sarcastic intent in a broad range of statements.
[0016] Another objective of the present disclosure is to provide adaptability for various applications, including customer service automation, virtual assistants, and content moderation tools, ensuring effective sarcasm recognition across multiple industries.
[0017] Another objective of the present disclosure is to minimize errors in sarcasm classification by improving the ability to detect sarcasm in ambiguous statements, ensuring reliable identification even when sarcasm is subtle or context-dependent.
[0018] Another objective of the present disclosure is to provide a scalable sarcasm detection system that can be integrated into different platforms without being limited by specific datasets, allowing for continuous improvements in understanding diverse conversational patterns.
[0019] Another objective of the present disclosure is to support multilingual sarcasm detection by enabling the system to recognize sarcastic expressions in various languages, dialects, and communication styles, promoting inclusivity in automated interactions.
[0020] Yet another objective of the present disclosure is to ensure robustness against misleading statements and adversarial responses by making sarcasm detection resilient to deceptive language patterns, preventing intentional manipulation of sarcasm identification processes.
[0021] In light of the above, in one aspect of the present disclosure, a contextual sarcasm identification system is disclosed herein. The system comprises a double bidirectional encoder leveraging representations from transformers architecture, wherein the double bidirectional encoder processes input text samples to generate contextual embeddings that capture nuanced relationships including irony and implied meanings. The system includes a plurality of processing units connected to the double bidirectional encoder, the plurality of processing units configured for generating contextual embeddings and performing classification based on the contextual embeddings, wherein a first processing unit generates the contextual embeddings and a second processing unit performs classification utilizing the contextual embeddings to enhance robustness against adversarial inputs and contextual ambiguities. The system also includes a dataset repository connected to the plurality of processing units, the dataset repository storing a plurality of training samples incorporating adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences, wherein the dataset repository facilitates model training to enhance robustness against adversarial inputs and contextual ambiguities. The system also includes a memory unit connected to the plurality of processing units, the memory unit storing a trained sarcasm detection model comprising concatenated outputs from the first processing unit and the second processing unit, wherein the concatenation enhances adaptability across linguistic, cultural, and contextual variations. The system also includes a communication network connected to the plurality of processing units, the communication network configured for receiving input text samples and transmitting processed results, wherein the processed results classify each input as sarcastic or non-sarcastic based on a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912. The system also includes an output unit connected to the communication network, the output unit receiving processed results from the communication network and presenting classification outcomes in real time, enabling dynamic sarcasm detection and contextual evaluation in conversational contexts. The system also includes a control unit operatively coupled to the plurality of processing units, and a plurality of system components wherein the control unit executes model inference operations ensuring scalability and optimal performance across diverse communication styles.
[0022] In one embodiment, the first processing unit extracts deep semantic features from the input text sample by applying contextual embedding generation techniques.
[0023] In one embodiment, the second processing unit applies a classification mechanism utilizing a refined attention mechanism and dropout regularization.
[0024] In one embodiment, the dataset repository comprises a structured dataset incorporating multiple linguistic styles, including formal, informal, slang, and culturally diverse sentence structures, ensuring adaptability across demographic variations.
[0025] In one embodiment, the trained sarcasm detection model integrates concatenated contextual and classification embeddings, improving resistance to adversarial misclassifications and deceptive textual constructs.
[0026] In one embodiment, the plurality of processing units implements an adversarial training mechanism to ensure robustness against deliberately misleading inputs, reinforcing the system’s resilience in complex conversational scenarios.
[0027] In one embodiment, the system further comprises an attention alignment module operatively connected to the double bidirectional encoder, wherein the attention alignment module dynamically reweights attention distributions across input tokens to prioritize sarcasm-indicative patterns.
[0028] In one embodiment, the plurality of processing units further comprises an embedding fusion unit disposed between the first processing unit and the second processing unit, the embedding fusion unit configured for integrating syntactic and semantic embeddings to enhance latent sarcasm feature representation.
[0029] In one embodiment, the system further comprises a sarcasm calibration unit operatively coupled to the output unit and the memory unit, wherein the sarcasm calibration unit adjusts the sarcasm prediction scores based on a learned sarcasm sensitivity profile derived from historical user inputs.
[0030] In light of the above, in one aspect of the present disclosure, a contextual sarcasm identification method is disclosed herein. The method comprises processing input text samples using a double bidirectional encoder that leverages representations from transformers architecture to generate contextual embeddings representing linguistic features. The method includes transmitting the contextual embeddings to a plurality of processing units, the plurality of processing units including a first processing unit and a second processing unit, the first processing unit generating refined contextual embeddings, and the second processing unit classifying the refined contextual embeddings to detect sarcastic or non-sarcastic intent. The method also includes retrieving training samples using the plurality of processing units from a dataset repository connected to the plurality of processing units, the dataset repository storing a plurality of training samples comprising adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences. The method also includes storing a trained sarcasm detection model in a memory unit connected to the plurality of processing units, the trained sarcasm detection model comprising concatenated outputs from the first processing unit and the second processing unit. The method also includes receiving real-time input text samples using a communication network connected to the plurality of processing units and transmitting classified results to an output unit connected to the communication network. The method also includes presenting real-time classification results using the output unit, enabling dynamic sarcasm detection based on context and sentiment characteristics, wherein the classification achieves a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912. The method also includes executing a real-time inference process using a control unit operatively coupled to the plurality of system components, the real-time inference process integrating multi-dimensional contextual patterns, adversarial resistance, and demographic adaptability, thereby forming a unified sarcasm detection architecture.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of a contextual sarcasm identification system and method thereof, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart of a contextual sarcasm identification system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of a contextual sarcasm identification method, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 4 illustrates a perspective view of the contextual sarcasm identification model, in accordance with an exemplary embodiment of the present disclosure; and
[0040] FIG. 5 illustrates a line graph of the performance of the D-BERT model, in accordance with an exemplary embodiment of the present disclosure.
[0041] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0042] The contextual sarcasm identification system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0043] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0044] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0045] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0046] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0047] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0048] Referring now to FIG. 1 to FIG. 5 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of a contextual sarcasm identification system and method thereof 100, in accordance with an exemplary embodiment of the present disclosure.
[0049] The system 100 may include a double bidirectional encoder 102 leveraging representations from transformers architecture, wherein the double bidirectional encoder 102 processes input text samples to generate contextual embeddings that capture nuanced relationships including irony and implied meanings. The system 100 may also include a plurality of processing units 104 connected to the double bidirectional encoder 102, the plurality of processing units 104 configured for generating contextual embeddings and performing classification based on the contextual embeddings, wherein a first processing unit 106 generates the contextual embeddings and a second processing unit 108 performs classification utilizing the contextual embeddings to enhance robustness against adversarial inputs and contextual ambiguities. The system 100 may also include a dataset repository 110 connected to the plurality of processing units 104, the dataset repository 110 storing a plurality of training samples incorporating adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences, wherein the dataset repository 110 facilitates model training to enhance robustness against adversarial inputs and contextual ambiguities. The system 100 may also include a memory unit 112 connected to the plurality of processing units 104, the memory unit 112 storing a trained sarcasm detection model comprising concatenated outputs from the first processing unit 106 and the second processing unit 108, wherein the concatenation enhances adaptability across linguistic, cultural, and contextual variations. The system 100 may also include a communication network 114 connected to the plurality of processing units 104, the communication network 114 configured for receiving input text samples and transmitting processed results, wherein the processed results classify each input as sarcastic or non-sarcastic based on a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912. The system 100 may also include an output unit 116 connected to the communication network 114, the output unit 116 receiving processed results from the communication network 114 and presenting classification outcomes in real time, enabling dynamic sarcasm detection and contextual evaluation in conversational contexts. The system 100 may also include a control unit 118 operatively coupled to the plurality of processing units 104, and a plurality of system components, wherein the control unit 118 executes model inference operations ensuring scalability and optimal performance across diverse communication styles.
[0050] The first processing unit 106 extracts deep semantic features from the input text sample by applying contextual embedding generation techniques.
[0051] The second processing unit 108 applies a classification mechanism utilizing a refined attention mechanism and dropout regularization.
[0052] The dataset repository 110 comprises a structured dataset incorporating multiple linguistic styles, including formal, informal, slang, and culturally diverse sentence structures, ensuring adaptability across demographic variations.
[0053] The trained sarcasm detection model stored in the memory unit 112 integrates concatenated contextual and classification embeddings, improving resistance to adversarial misclassifications and deceptive textual constructs.
[0054] The plurality of processing units 104 implements an adversarial training mechanism to ensure robustness against deliberately misleading inputs, reinforcing the system’s resilience in complex conversational scenarios.
[0055] The system 100 further comprises an attention alignment module operatively connected to the double bidirectional encoder 102, wherein the attention alignment module dynamically reweights attention distributions across input tokens to prioritize sarcasm-indicative patterns.
[0056] The processing units 104 further comprises an embedding fusion unit disposed between the first processing unit 106 and the second processing unit 108, the embedding fusion unit configured for integrating syntactic and semantic embeddings to enhance latent sarcasm feature representation.
[0057] The system 100 further comprises a sarcasm calibration unit operatively coupled to the output unit 116 and the memory unit 112, wherein the sarcasm calibration unit adjusts the sarcasm prediction scores based on a learned sarcasm sensitivity profile derived from historical user inputs.
[0058] The method 100 may include processing input text samples using a double bidirectional encoder 102 that leverages representations from transformers architecture to generate contextual embeddings representing linguistic features. The method 100 may also include transmitting the contextual embeddings to a plurality of processing units 104, the plurality of processing units 104 including a first processing unit 106 and a second processing unit 108, the first processing unit 106 generating refined contextual embeddings, and the second processing unit 108 classifying the refined contextual embeddings to detect sarcastic or non-sarcastic intent. The method 100 may also include retrieving training samples using the plurality of processing units 104 from a dataset repository 110 connected to the plurality of processing units 104, the dataset repository 110 storing a plurality of training samples comprising adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences. The method 100 may also include storing a trained sarcasm detection model in a memory unit 112 connected to the plurality of processing units 104, the trained sarcasm detection model comprising concatenated outputs from the first processing unit 106 and the second processing unit 108. The method 100 may also include receiving real-time input text samples using a communication network 114 connected to the plurality of processing units 104 and transmitting classified results to an output unit 116 connected to the communication network 114. The method 100 may also include presenting real-time classification results using the output unit 116, enabling dynamic sarcasm detection based on context and sentiment characteristics, wherein the classification achieves a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912. The method 100 may also include executing a real-time inference process using a control unit 118 operatively coupled to the plurality of system components, the real-time inference process integrating multi-dimensional contextual patterns, adversarial resistance, and demographic adaptability, thereby forming a unified sarcasm detection architecture.
[0059] The double bidirectional encoder 102 leverages representations from transformers architecture to process input text samples for robust sarcasm detection. The double bidirectional encoder 102 extracts deep contextual features from text by capturing nuanced relationships including irony and implied meanings. The double bidirectional encoder 102 operates continuously on diverse linguistic and cultural inputs, generating high-dimensional embeddings that represent semantic and syntactic information. The double bidirectional encoder 102 integrates internal regularization techniques to maintain resistance against adversarial inputs. The double bidirectional encoder 102 transfers the produced embeddings seamlessly to the plurality of processing units 104 for further analysis. The double bidirectional encoder 102 adapts dynamically to varied communication styles and is critical in forming a foundation for accurate contextual understanding.
[0060] The plurality of processing units 104 is connected to the double bidirectional encoder 102 and operates to perform sophisticated analysis on the contextual embeddings. The plurality of processing units 104 comprises multiple units that share computational tasks, including one unit focused on deep embedding generation and another on performing classification. The plurality of processing units 104 orchestrates efficient processing, ensuring that each text input is analyzed for semantic nuance and subtle sarcasm indicators. The plurality of processing units 104 coordinates with the dataset repository 110 and the memory unit 112 to sustain robust model performance. The plurality of processing units 104 thereby reinforces processing accuracy while managing contextual ambiguities and adversarial scenarios across heterogeneous datasets.
[0061] The first processing unit 106 is dedicated to generating refined contextual embeddings from the raw outputs of the double bidirectional encoder 102. The first processing unit 106 implements advanced techniques to extract deep semantic features from input text samples and enhance detailed representation of linguistic subtleties. The first processing unit 106 processes embedding vectors to capture intricate contextual cues essential for sarcasm detection. The first processing unit 106 operates under dynamic conditions, continuously adapting to variations in communication styles and demographic nuances. The first processing unit 106 supports overall system performance by providing high-quality refined embeddings that feed into subsequent classification operations in the second processing unit 108.
[0062] The second processing unit 108 receives refined contextual embeddings from the first processing unit 106 and performs precise classification to distinguish between sarcastic and non-sarcastic inputs. The second processing unit 108 employs a robust classification mechanism that integrates attention mechanisms and dropout regularization to mitigate overfitting. The second processing unit 108 analyses the semantic weight and contextual reliability of each embedding to produce accurate outputs. The second processing unit 108 emphasizes adversarial resilience by processing inputs that exhibit ambiguous contextual signals. The second processing unit 108 is crucial in achieving a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912, ensuring reliable and dynamic sarcasm detection.
[0063] The dataset repository 110 stores a vast collection of training samples that include adversarial responses, diverse demographic variations, cultural differences, emotional expressions, and distinct linguistic structures. The dataset repository 110 is connected to the plurality of processing units 104, ensuring that continuous model training and refinement occur using an extensive and varied dataset. The dataset repository 110 plays an essential role in enhancing model robustness against contextual ambiguities and misleading inputs. The dataset repository 110 supplies representative data that supports adaptive threshold tuning in classification tasks while maintaining high performance. The dataset repository 110 ensures that all facets of real-world communication are captured, thereby facilitating effective sarcasm detection and contextual understanding across different user groups.
[0064] The memory unit 112 is connected to the plurality of processing units 104 and stores a trained sarcasm detection model composed of concatenated outputs from the first processing unit 106 and the second processing unit 108. The memory unit 112 maintains high-dimensional embedding data that enhances adaptability across diverse linguistic, cultural, and contextual variations. The memory unit 112 preserves intermediate outputs and calibration parameters necessary for robust model inference and continuous learning. The memory unit 112 plays a critical role in retaining critical model weights, ensuring reliable recall and rapid updating during both training and real-time inference. The memory unit 112 ultimately supports overall system stability and scalability.
[0065] The communication network 114 is connected to the plurality of processing units 104 and facilitates secure, real-time transmission of encrypted stress-related data and processed text samples. The communication network 114 ensures low-latency data exchange between all components, including the double bidirectional encoder 102, the dataset repository 110, and the control unit 118. The communication network 114 guarantees that refined embeddings, classification outputs, and model updates flow seamlessly to the output unit 116 for real-time presentation.
[0066] The output unit 116 is connected to the communication network 114 and receives processed results from the plurality of processing units 104. The output unit 116 presents real-time classification outcomes, indicating whether each input text is classified as sarcastic or non-sarcastic based on a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912. The output unit 116 displays results clearly for dynamic contextual evaluation, ensuring that users have immediate access to sarcasm detection outcomes.
[0067] The control unit 118 is operatively coupled to the plurality of processing units 104, the dataset repository 110, the memory unit 112, the communication network 114, and the output unit 116. The control unit 118 governs model inference operations, orchestrating the entire processing pipeline for robust sarcasm detection. The control unit 118 dynamically schedules input parsing, contextual embedding generation, classification operations, and result transmission to ensure seamless and scalable performance across diverse communication styles. The control unit 118 continuously monitors system performance and triggers iterative model updates to maintain optimal accuracy and responsiveness.
[0068] FIG. 2 illustrates a flowchart of a contextual sarcasm identification system, in accordance with an exemplary embodiment of the present disclosure.
[0069] At 202, receiving input text samples through the communication network for sarcasm detection.
[0070] At 204, processing input text using a double bidirectional encoder to generate contextual embeddings.
[0071] At 206, forwarding the contextual embeddings to a plurality of processing units for analysis and classification.
[0072] At 208, accessing the dataset repository to reference diverse linguistic, cultural, and adversarial samples during processing.
[0073] At 210, generating classification output using concatenated results stored in the memory unit.
[0074] At 212, transmitting classified results as sarcastic or non-sarcastic through the communication network to the output unit.
[0075] At 214, managing inference operations and component coordination using the control unit to ensure accuracy and scalability.
[0076] FIG. 3 illustrates a flowchart of a contextual sarcasm identification method, in accordance with an exemplary embodiment of the present disclosure.
[0077] At 302, processing input text samples using a double bidirectional encoder that leverages representations from transformers architecture to generate contextual embeddings representing linguistic features.
[0078] At 304, transmitting the contextual embeddings to a plurality of processing units, the plurality of processing units including a first processing unit and a second processing unit, the first processing unit generating refined contextual embeddings, and the second processing unit classifying the refined contextual embeddings to detect sarcastic or non-sarcastic intent.
[0079] At 306, retrieving training samples using the plurality of processing units from a dataset repository connected to the plurality of processing units, the dataset repository storing a plurality of training samples comprising adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences.
[0080] At 308, storing a trained sarcasm detection model in a memory unit connected to the plurality of processing units, the trained sarcasm detection model comprising concatenated outputs from the first processing unit and the second processing unit.
[0081] At 310, receiving real-time input text samples using a communication network connected to the plurality of processing units and transmitting classified results to an output unit connected to the communication network.
[0082] At 312, presenting real-time classification results using the output unit, enabling dynamic sarcasm detection based on context and sentiment characteristics, wherein the classification achieves a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912.
[0083] At 314, executing a real-time inference process using a control unit operatively coupled to the plurality of system components, the real-time inference process integrating multi-dimensional contextual patterns, adversarial resistance, and demographic adaptability, thereby forming a unified sarcasm detection architecture.
[0084] FIG. 4 illustrates a perspective view of the contextual sarcasm identification model, in accordance with an exemplary embodiment of the present disclosure.
[0085] Sarcasm detection data sets 402 contain a comprehensive collection of textual samples gathered from various sources to support model training for identifying sarcastic expressions. sarcasm detection data sets 402 include diverse linguistic data with emotional nuances and contextual variations, providing a robust foundation for developing models with enhanced sensitivity to subtle sarcasm cues and adversarial phrasing.
[0086] Semeval-2020 404 provides a structured corpus of sarcastic and non-sarcastic text samples curated during the SemEval workshop. Semeval-2020 404 offers annotated data reflecting diverse linguistic usages and cultural contexts, thereby assisting the model in understanding varied expressions of irony and implied meaning. Semeval-2020 404 ensures detailed annotations that support precise model evaluation.
[0087] Reddit 406 comprises a collection of textual comments and posts from reddit forums, capturing informal language and spontaneous sarcasm. Reddit 406 offers a rich and varied dataset where diverse conversational dynamics and cultural influences are reflected. Reddit 406 contributes essential real-world variability, enhancing the model’s ability to generalize and detect subtle sarcasm in online discourse.
[0088] Onion 408 consists of satirical news articles and humorous texts published by the Onion, characterized by overt irony and exaggerated language. Onion 408 presents challenging linguistic structures that test the model’s capacity for deep contextual understanding and sarcasm detection. Onion 408 ensures that the system encounters deliberately hyperbolic language and layered meanings during model training.
[0089] Twitter 410 gathers brief textual messages from the Twitter platform, reflecting informal communication and diverse stylistic expressions of sarcasm. Twitter 410 contains data that capture rapid conversational exchanges and cultural slang, contributing to the model’s robust performance under short and contextually ambiguous conditions. Twitter 410 enhances model adaptability to varied digital communication styles.
[0090] Mixed data set with 235,480 samples 412 offers an extensive aggregation of text from multiple sources, incorporating adversarial responses, demographic variations, emotional expressions, and diverse cultural contexts. Mixed data set with 235,480 samples 412 provides comprehensive training material that drives superior performance and robustness against the challenges posed by complex and ambiguous sarcastic expressions in real-world scenarios.
[0091] BERT embedding 414 represents the contextual feature representation generated by a pre-trained BERT model. BERT embedding 414 captures deep semantic and syntactic information from input text, enabling the extraction of nuanced meaning essential for identifying sarcasm. BERT embedding 414 forms the basis for subsequent processing units by delivering high-dimensional vectors that reflect rich textual representations.
[0092] Token embedding (2,768) 416 encodes individual tokens in the input text into fixed-length vectors of dimension 768, preserving semantic details necessary for contextual analysis. Token embedding (2,768) 416 transforms raw textual tokens into a format conducive to deep learning, facilitating the extraction of linguistic features vital for discerning subtle sarcasm cues in varied textual inputs.
[0093] Position embedding (512,768) 418 encodes the positional information of each token within the sequence into 768-dimensional vectors over a maximum sequence length of 512. Position embedding (512,768) 418 ensures that the model captures sequential relationships and structural context critical for understanding word order and syntactic dependencies, thereby enhancing the detection of sarcasm in complex sentence structures.
[0094] WORD embedding (30522,768) 420 maps each word from a vocabulary of 30,522 tokens into a fixed 768-dimensional vector space, preserving semantic relationships between words. WORD embedding (30522,768) 420 enables a comprehensive capture of lexical semantics, which, when combined with contextual features, contributes significantly to the nuanced interpretation and classification of sarcastic text.
[0095] 12BERT ENCODER 422 constitutes a multi-layer transformer encoder stack consisting of 12 transformer layers that process and refine contextual embeddings from the double bidirectional encoder. 12BERT ENCODER 422 enhances deep semantic comprehension by aggregating information across multiple layers, thereby facilitating robust contextual understanding and improved sarcasm detection in diverse linguistic scenarios.
[0096] 12BERT classification 424 comprises 12 transformer layers specifically configured for the classification task, processing refined embeddings to determine sarcasm. 12BERT classification 424 employs multi-head self-attention and layer normalization techniques to accurately differentiate sarcastic statements from non-sarcastic ones, thereby boosting the overall accuracy and resilience of the classification process in challenging adversarial contexts.
[0097] Attention 426 is implemented as a multi-head attention mechanism that assigns varying weights to different parts of the input text, optimizing the capture of salient features necessary for sarcasm detection. Attention 426 dynamically focuses processing resources on key linguistic tokens that contribute to the interpretation of sarcasm, thereby enabling the system to emphasize critical contextual cues and improve overall detection precision.
[0098] Self-attention 428 provides a mechanism to evaluate the interdependencies among input tokens within a sequence, forming contextual representations that capture relationships between all tokens simultaneously. Self-attention 428 ensures that every token influences the representation of every other token, thereby enriching the model’s understanding of context and enhancing the ability to detect subtle sarcastic nuances within complex textual interactions.
[0099] Q(in,out) (768,768) 430 defines the query projection matrix which transforms input embeddings into a query space of dimension 768. Q(in,out) (768,768) 430 is essential for the self-attention mechanism, as it ensures that the transformed query vectors effectively match with key vectors, facilitating precise attention calculations that contribute to robust contextual representation for sarcasm detection.
[0100] K(in,out) (768,768) 432 specifies the key projection matrix that transforms input embeddings into a key space of dimension 768. K(in,out) (768,768) 432 works in conjunction with the query projection matrix to determine the attention weights, ensuring accurate alignment between query and key representations, which is crucial for identifying subtle linguistic cues essential for effective sarcasm classification.
[0101] V(in,out) (768,768) 434 defines the value projection matrix which transforms input embeddings into a value space of dimension 768. V(in,out) (768,768) 434 operates jointly with query and key matrices within the self-attention mechanism to produce a weighted sum of values, thereby enhancing the representation of contextual features necessary for robust sarcasm detection in complex textual data.
[0102] Liear Attention (768,768) 436 implements a linear projection approach to further refine attention outputs by applying a linear transformation across the attention weights of dimension 768. Liear Attention (768,768) 436 enhances computational efficiency while preserving crucial contextual relationships, thereby contributing to the overall adaptability and responsiveness of the sarcasm detection system.
[0103] Layer Normalize (768,) 438 applies normalization across a 768-dimensional feature vector, ensuring consistent scaling and distribution of activation values. Layer Normalize (768,) 438 stabilizes and accelerates the training process within transformer layers by standardizing inputs, thereby contributing to improved convergence and performance in detecting subtle sarcasm expressions across diverse text samples.
[0104] Liear Inter-Attention (768,3072) 440 represents a linear transformation that projects 768-dimensional attention vectors into a higher-dimensional space of 3072 dimensions to capture broader contextual features. Liear Inter-Attention (768,3072) 440 enhances the granularity of feature interactions during inter-attention operations, contributing to more robust discrimination of complex sarcasm signals within the text.
[0105] Liear Inter-Attention (3072,768) 442 provides a complementary linear transformation that reduces the dimensionality of inter-attention features from 3072 to 768 dimensions. Liear Inter-Attention (3072,768) 442 compresses rich contextual interactions while preserving essential information, thereby facilitating efficient and accurate synthesis of interdependent linguistic features for sarcasm detection.
[0106] Layer Normalize (768.) 444 standardizes 768-dimensional feature vectors post-inter-attention, maintaining consistency in the range and scale of activations. Layer Normalize (768.) 444 ensures stable training and improved convergence in transformer layers, enabling effective balancing of learned representations for precise sarcasm identification across diverse linguistic inputs.
[0107] Pool - (768,768) 446 aggregates information across temporal sequences by performing a pooling operation on 768-dimensional vectors to generate a fixed-size representation. Pool - (768,768) 446 condenses variable-length contextual embeddings into a uniform output, thereby providing a comprehensive summary necessary for robust classification and interpretation of sarcasm across diverse text samples.
[0108] SoftMax 448 applies a normalization function over a set of output logits to produce a probability distribution across classes. SoftMax 448 ensures that the output probabilities sum to one and enables clear, unambiguous classification of each input as sarcastic or non-sarcastic, supporting high accuracy in the final output from the sarcasm detection model.
[0109] (out) (2) 450 represents the final output layer producing a two-dimensional probability vector corresponding to the classes sarcastic and non-sarcastic. (out) (2) 450 delivers definitive classification results based on optimized model parameters, ensuring that the assigned probabilities reflect the high validation accuracy and weighted F1-score established during model training.
[0110] Concatination 452 fuses multiple feature vectors derived from different processing stages such as contextual embeddings and classification outputs into a unified representation. Concatination 452 enhances the overall expressiveness and adaptability of the sarcasm detection model by integrating diverse linguistic features into a single comprehensive vector, thereby improving final classification performance and robustness against adversarial inputs.
[0111] FIG. 5 illustrates a line graph of the performance of the D-BERT model, in accordance with an exemplary embodiment of the present disclosure.
[0112] A bar graph representing the performance of the double bidirectional encoder using three evaluation metrics, namely precision, recall, and F1 score, across two categories, namely sarcasm and non-sarcasm. The non-sarcasm category achieves 0.92 in both precision and F1 score, and 0.91 in recall. The sarcasm category achieves 0.89 in precision, and 0.90 in both recall and F1 score, indicating high classification effectiveness.
[0113] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0114] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0115] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0116] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0117] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A contextual sarcasm identification system (100) comprising:
a double bidirectional encoder (102) leveraging representations from transformers architecture, wherein the double bidirectional encoder (102) processes input text samples to generate contextual embeddings that capture nuanced relationships including irony and implied meanings;
a plurality of processing units (104) connected to the double bidirectional encoder (102), the plurality of processing units (104) configured for generating contextual embeddings and performing classification based on the contextual embeddings, wherein a first processing unit (106) generates the contextual embeddings and a second processing unit (108) performs classification utilizing the contextual embeddings to enhance robustness against adversarial inputs and contextual ambiguities;
a dataset repository (110) connected to the plurality of processing units (104), the dataset repository (110) storing a plurality of training samples incorporating adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences, wherein the dataset repository (110) facilitates model training to enhance robustness against adversarial inputs and contextual ambiguities;
a memory unit (112) connected to the plurality of processing units (104), the memory unit (112) storing a trained sarcasm detection model comprising concatenated outputs from the first processing unit (106) and the second processing unit (108), wherein the concatenation enhances adaptability across linguistic, cultural, and contextual variations;
a communication network (114) connected to the plurality of processing units (104), the communication network (114) configured for receiving input text samples and transmitting processed results, wherein the processed results classify each input as sarcastic or non-sarcastic based on a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912;
an output unit (116) connected to the communication network (114), the output unit (116) receiving processed results from the communication network (114) and presenting classification outcomes in real time, enabling dynamic sarcasm detection and contextual evaluation in conversational contexts; and
a control unit (118) operatively coupled to the plurality of processing units (104), and a plurality of system components, wherein the control unit (118) executes model inference operations ensuring scalability and optimal performance across diverse communication styles.
2. The system (100) as claimed in claim 1, wherein the first processing unit (106) extracts deep semantic features from the input text sample by applying contextual embedding generation techniques.
3. The system (100) as claimed in claim 1, wherein the second processing unit (108) applies a classification mechanism utilizing a refined attention mechanism and dropout regularization.
4. The system (100) as claimed in claim 1, wherein the dataset repository (110) comprises a structured dataset incorporating multiple linguistic styles, including formal, informal, slang, and culturally diverse sentence structures, ensuring adaptability across demographic variations.
5. The system (100) as claimed in claim 1, wherein the trained sarcasm detection model stored in the memory unit (112) integrates concatenated contextual and classification embeddings, improving resistance to adversarial misclassifications and deceptive textual constructs.
6. The system (100) as claimed in claim 1, wherein the plurality of processing units (104) implements an adversarial training mechanism to ensure robustness against deliberately misleading inputs, reinforcing the system’s resilience in complex conversational scenarios.
7. The system (100) as claimed in claim 1, wherein the system (100) further comprises an attention alignment module operatively connected to the double bidirectional encoder (102), wherein the attention alignment module dynamically reweights attention distributions across input tokens to prioritize sarcasm-indicative patterns.
8. The system (100) as claimed in claim 1, wherein the processing units (104) further comprises an embedding fusion unit disposed between the first processing unit (106) and the second processing unit (108), the embedding fusion unit configured for integrating syntactic and semantic embeddings to enhance latent sarcasm feature representation.
9. The system (100) claimed in claim 1, wherein the system (100) further comprises a sarcasm calibration unit operatively coupled to the output unit (116) and the memory unit (112), wherein the sarcasm calibration unit adjusts the sarcasm prediction scores based on a learned sarcasm sensitivity profile derived from historical user inputs.
10. A contextual sarcasm identification method (100) comprising:
processing input text samples using a double bidirectional encoder (102) that leverages representations from transformers architecture to generate contextual embeddings representing linguistic features;
transmitting the contextual embeddings to a plurality of processing units (104), the plurality of processing units (104) including a first processing unit (106) and a second processing unit (108), the first processing unit (106) generating refined contextual embeddings, and the second processing unit (108) classifying the refined contextual embeddings to detect sarcastic or non-sarcastic intent;
retrieving training samples using the plurality of processing units (104) from a dataset repository (110) connected to the plurality of processing units (104), the dataset repository (110) storing a plurality of training samples comprising adversarial responses, diverse demographic variations, emotional expressions, and linguistic differences;
storing a trained sarcasm detection model in a memory unit (112) connected to the plurality of processing units (104), the trained sarcasm detection model comprising concatenated outputs from the first processing unit (106) and the second processing unit (108);
receiving real-time input text samples using a communication network (114) connected to the plurality of processing units (104) and transmitting classified results to an output unit (116) connected to the communication network (114);
presenting real-time classification results using the output unit (116), enabling dynamic sarcasm detection based on context and sentiment characteristics, wherein the classification achieves a validation accuracy exceeding ninety-one percent and a weighted F1-score exceeding 0.912;
executing a real-time inference process using a control unit (118) operatively coupled to the plurality of system components, the real-time inference process integrating multi-dimensional contextual patterns, adversarial resistance, and demographic adaptability, thereby forming a unified sarcasm detection architecture.