Team IITP - AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection (2022)

Abstract: Computational comprehension and identifying emotional components in language have been critical in enhancing human-computer connection in recent years. The WASSA 2022 Shared Task introduced four tracks and released a dataset of news stories: Track-1 for Empathy and Distress Prediction, Track-2 for Emotion classification, Track-3 for Personality prediction, and Track-4 for Interpersonal Reactivity Index prediction at the essay level. This paper describes our participation in the WASSA 2022 shared task on the tasks mentioned above. We developed multi-task deep learning methods to address Tracks 1 and 2 and machine learning models for Track 3 and 4. Our developed systems achieved average Pearson scores of 0.483, 0.05, and 0.08 for Track 1, 3, and 4, respectively, and a macro F1 score of 0.524 for Track 2 on the test set. We ranked 8th, 11th, 2nd and 2nd for tracks 1, 2, 3, and 4 respectively.

## introduction with the growing interest in the human-computer interface, emotions are considered for listing differences between machines and living beings. humans' inherent knowledge of these emotions is hard to pass on to machines. hence, the introduced wassa 2022 shared task of empathy detection, emotion classification, and personality detection is challenging. although some research has been done by @xcite and @xcite , they have several important shortcomings, such as the simplistic definition of empathy and the lack of these corpora in the public domain. the wassa 2022 shared task consists of the following four major sub-tasks: ## related work because of language disparities across locales, empathy and distress might also vary dependent on demographics @xcite . more recently, @xcite proposed a demographic-aware empathy modeling framework based on bidirectional encoder representations from transformers (bert) and demographic characteristics. the first publicly accessible goldstandard dataset for text-based empathy and distress prediction was introduced by @xcite . @xcite investigated a multitask roberta-based bi-encoder paradigm for comprehending empathy in text-based health support. zhou and jurgens (2020) investigated the link between distress, condolence, and empathy in online support groups using nested regression models. many research @xcite have given various strategies for emotion recognition. the effectiveness of using transformer encoders for emotion detection was investigated by @xcite . the wassa-2021 shared task @xcite addressed the prediction of empathy (track 1) and emotion (track 2) in text. personality detection studies @xcite utilising computational approaches have lately gained traction, particularly language models like bert @xcite . majority of works on this issue have employed statistical analysis @xcite and feature engineering @xcite . ## data the shared task organizers made available an expanded version of the dataset from @xcite . ## system description this section describes the various developed methodologies to address the different tasks in the wassa 2022 shared task. ## results and discussion we discuss the hyper-parameters in our experiments, results, and analysis in this section. we report the results from our experiments considering the development set as our test dataset, as the gold standard annotations of the test are withheld in the shared task of wassa-2022. ## conclusion this paper presents our approaches to address the various tasks introduced in the wassa 2022 shared task for empathy detection, emotion classification, and personality detection. to exploit the commonality among correlated tasks such as empathy and distress and emotion with empathy and distress, we developed multi-task systems built on pre-trained bert models for -(a) empathy and distress detection tasks; (b). emotion classification (primary task) and empathy and distress classification (auxiliary tasks). we also presented svm algorithms trained on various demographic features to predict personality traits and interpersonal reactivity index scores. we empirically observed how jointly learning correlated tasks such as empathy and distress, emotion with empathy and distress, helps to improve overall system performance. our developed systems achieved average pearson scores of 0.483, 0.05, and 0.08 for track 1, 3 and 4, respectively, and a macro f1 score of 0.524 for track 2 on the test set. we ranked 8 th , 11 th , 2 nd and 2 nd for the tracks 1, 2, 3 and 4 respectively. we want to improve our multi-tasking-based systems in the future by adding lexicon features from available lexical resources alongside textual input for the emp and emo tasks. we also want to develop an effective technique for combining contextual information from an author's essays with demographic data to predict per and iri scores.