NULL at S em E val-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning (2022)

Abstract: The intended sarcasm cannot be understood until the listener observes that the text’s literal meaning violates truthfulness. Consequently, words and meanings play an essential role in specifying sarcasm. Enriched feature extraction techniques were proposed to capture both words and meanings in the contexts. Due to the overlapping features in sarcastic and non-sarcastic texts, a CNN model extracts local features from the combined class-dependent statistical embedding of sarcastic texts with contextualized embedding. Another component BiLSTM extracts long dependencies from combined non-sarcastic statistical and contextualized embeddings. This work combines a classifier that uses the combined high-level features of CNN and BiLSTM for sarcasm detection to produce the final predictions. The experimental analysis presented in this paper shows the effectiveness of the proposed method.

## introduction sarcasm detection is a specific case of sentiment analysis where the focus is on detecting sarcasm in text. therefore, the task is to detect if a given text is sarcastic or not. according to @xcite , sarcasm refers to the use of words that mean the opposite of what you want to say, especially to insult someone, show irritation, or provide humor. however, @xcite define sarcasm as a form of irony marked by a discrepancy between the literal and intended meanings of an utterance, through which the speaker usually manifests a hostile, derogatory, or contemptuous attitude. generally, sarcasm tweets/texts are the utterances of a positive statement with harmful intent, and since the intent is hard to detect not only for computers but also for humans, it has attracted a considerable body of research in the natural language processing field to study opinions given by the users of online social media platforms such as twitter, facebook, reddit, and instagram. in the semeval 2022 at task 6: isarcasmeval @xcite , the goal is to identify intended sarcasm in both english and arabic languages. this task consists of 3 subtasks, and we will focus on subtask a defined as: subtask a: given a text, determine whether it is sarcastic or non-sarcastic. since sarcasm is an utterance of a statement with negative intent (e.g., he has the best taste in music.") and an admiring tone (e.g., "she always makes dry jokes."). we hypothesized that contextualized representation might not be enough to represent intent when considering the sentiment analysis challenge. therefore, we combined the representation of stylistically statistical and contextualized word embeddings using deep neural networks (dnns) as a high-level feature extractor and classifier for the task. in this work, we studied the effect of the combination of stylistic and contextualized representation. the rest of the article is organized as follows. we first describe sarcasm studies in section 2. section 3 presents the proposed methodology. sections 4 and 5 describe experimental setups and results. moreover, in section 6 we made the discussion. finally, in section 7 we conclude the article. ## related works sarcasm detection research has seen a significant surge in interest in the past few years @xcite @xcite @xcite . at early stages, most works considered content as the only source for identifying sarcasm @xcite . to differentiate between sarcasm and non-sarcasm handcrafted rules and features have been applied @xcite @xcite . for instance, @xcite used seven sets of lexical features to detect sarcasm by its inner structure. frequency and structure (i.e., length, punctuation, emoticons) are two examples of sarcasm features. however, the model based on these stylistic features shows promising results but lacks external knowledge (i.e., common sense). our proposed method incorporates stylistic features with xlm-roberta model to cope with the limitations of prior methods. most recently, dominant approaches are context-based, utilizing background knowledge and contextual dependencies as prior knowledge about the event mentioned in a sarcastic context @xcite @xcite . @xcite provided empirical evidence that to make judgments concerning ironic intent annotators require additional contextual information. hazarika et al. is one of a few works that tried to capture the stylometric and personality features of the users and used user embeddings to encode stylometric and personality features of users combined by a content-based feature extractor (i.e., cnn) @xcite . this sarcasm detection model shows promising results on a large reddit corpus. the improvement in results by this work inspired us to investigate the effectiveness of incorporating content-based features for semeval-2022 task 6: isarcasmeval, but the problem we faced was the lack of data assigned to each user. as a result, instead of a users' unique features, we investigate the stylometric features of each class. in other words, we tried to find general styles of users who tried to write sarcastic texts by finding the distribution of their frequent words and other features provided in section 3. results provided in section 5 empirically demonstrate the effectiveness of this incorporation. the effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice, and they are also gaining attention in sarcasm detection. bert, roberta, and xlnet are examples that deliver significant improvements. for example, @xcite proposed recurrent cnn roberta (rcnn-roberta), which obtained %79 on the sarc dataset. based on its effectiveness and multilingualism, we utilized the roberta model for this research. ## model description in this section, we describe the details of our proposed deep learning model. the goal is to predict whether the given text is sarcastic or nonsarcastic. figure 1 depicts an overview of our proposed method. first, preprocessing (appendix a.1) is applied to the raw texts. next, character-based lower dimensionality statistical embedding (char-ldse) @xcite captures the stylistic information about sarcastic and nonsarcastic data in form of class-dependent features based on the term frequency of tokens in sarcastic and non-sarcastic classes. the calculated ldse features for sarcastic class in combination with contextualized representation are fed into convolutional neural networks (cnns) @xcite to extract local information of the texts. in another setting, the ldse for non-sarcastic class in combination with contextualized representation is fed into bidirectional long short-term memory (bilstm) @xcite to learn the long-dependent information in the texts. the features from cnn and bilstm layers combined for enhancing the feature extraction ability of the model. extracted high-level features are fed into the classifier. in the following, we describe each component elaborately. ## experimental setup dataset: for sarcastic or non-sarcastic detection tasks, the collected dataset where the sarcasm labels for texts are provided by authors themselves, thus eliminating labeling proxies. for hyperparameter tuning and model selection we split the training set into train and dev sets with a 10% split rate for both english and arabic languages. more information about the datasets ( • for english, the proposed model achieved 28th place among 43 teams, and 18th place among 32 teams for the arabic. regarding this phenomenon, the task suffers from type i error mostly. ## discussion type-1 error: in either language, non-sarcastic samples are predicted wrongly as sarcastic. type-1 error is the major source of error. analysis of english fp samples indicated that most of the samples were in response to a post or reply to another person and indeed, with no information about the text context nor the writer profile, this indicates a lack of primordial clues for sarcasm detection. to make an improvement in reducing the error two approaches can be taken. first, consider more information about the text context such as the tread or post. second, using a finer feature representation, since the lack of primordial clues leads to low-quality features. moreover, the analysis revealed the following findings in the data which can be taken into account to improve the results by reducing the error (all of the findings come from the analysis of 436 fp samples for the english test set). ## conclusion this paper presented our approach for semeval-2022 task 6: isarcasmeval: intended sarcasm detection in english and arabic. we investigated this problem by employing statistical and contextualized representations with deep learning techniques. we conducted the experimental and statistical analysis and presented the cnn-bilstm framework. the proposed studies in this paper show that considering stylistic features with deep learning frameworks is promising for intended sarcasm detection in both english and arabic languages. the overall stats of the data augmentation are presented in the