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Register a new userTeam Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models
فريق Phoenix في Wassa 2021: تحليل العاطفة حول القصص الإخبارية مع نماذج اللغة المدربة مسبقا
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العاطفة أساسية للإنسانية.تعد القدرة على إدراك التفاعلات الاجتماعية وتفهمها والاستجابة لها بطريقة تشبه الإنسان واحدة من أكثر القدرات المرجوة في الوكلاء الاصطناعي، خاصة في روبوتات الوسائط الاجتماعية.خلال السنوات القليلة الماضية، كانت التفاهم الحاسوبية والكشف عن الجوانب العاطفية في اللغة حيوية في تقدم التفاعل بين الإنسان والحاسوب.أصدرت المهمة المشتركة WASSA 2021 مجموعة بيانات من قصص الأخبار عبر مسارين، وتتبع 1 لتنبؤ التعاطف والمساء والمسار - 2 لتنبؤ العاطفة متعددة الأبعاد على مستوى المقال.نحن نصف إدخال نظامنا للمهمة المشتركة Wassa 2021 (لكل من المسار-1 والمسار -2)، حيث نستفيد من المعلومات من نماذج اللغة المدربة مسبقا للمهام الخاصة بالمسار.حققت نماذجنا المقترحة درجة متوسط بيرسون من 0.417، ونتيجة ماكرو F1 من 0.502 في المسار 1 والمسار 2، على التوالي.في لوحة المهمة المشتركة، حصلنا على المرتبة الرابعة في المسار 1 والمرتبة الثانية في المسار 2.
Emotion is fundamental to humanity. The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly in social-media bots. Over the past few years, computational understanding and detection of emotional aspects in language have been vital in advancing human-computer interaction. The WASSA Shared Task 2021 released a dataset of news-stories across two tracks, Track-1 for Empathy and Distress Prediction and Track-2 for Multi-Dimension Emotion prediction at the essay-level. We describe our system entry for the WASSA 2021 Shared Task (for both Track-1 and Track-2), where we leveraged the information from Pre-trained language models for Track-specific Tasks. Our proposed models achieved an Average Pearson Score of 0.417, and a Macro-F1 Score of 0.502 in Track 1 and Track 2, respectively. In the Shared Task leaderboard, we secured the fourth rank in Track 1 and the second rank in Track 2.
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This paper presents the results that were obtained from the WASSA 2021 shared task on predicting empathy and emotions. The participants were given access to a dataset comprising empathic reactions to news stories where harm is done to a person, group
, or other. These reactions consist of essays, Batson empathic concern, and personal distress scores, and the dataset was further extended with news articles, person-level demographic information (age, gender, ethnicity, income, education level), and personality information. Additionally, emotion labels, namely Ekman's six basic emotions, were added to the essays at both the document and sentence level. Participation was encouraged in two tracks: predicting empathy and predicting emotion categories. In total five teams participated in the shared task. We summarize the methods and resources used by the participating teams.
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This paper describes our submission for the WASSA 2021 shared task regarding the prediction of empathy, distress and emotions from news stories. The solution is based on combining the frequency of words, lexicon-based information, demographics of the
annotators and personality of the annotators into a linear model. The prediction of empathy and distress is performed using Linear Regression while the prediction of emotions is performed using Logistic Regression. Both tasks are performed using the same features. Our models rank 4th for the prediction of emotions and 2nd for the prediction of empathy and distress. These results are particularly interesting when considered that the computational requirements of the solution are minimal.
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Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive use
ful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax. In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders. Experiments in English show that ParaBART outperforms state-of-the-art sentence embedding models on unsupervised semantic similarity tasks. Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.
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