Shabnam Tafreshi


2022

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Proceedings of the Third Workshop on Insights from Negative Results in NLP
Shabnam Tafreshi | João Sedoc | Anna Rogers | Aleksandr Drozd | Anna Rumshisky | Arjun Akula
Proceedings of the Third Workshop on Insights from Negative Results in NLP

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Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Jeremy Barnes | Orphée De Clercq | Valentin Barriere | Shabnam Tafreshi | Sawsan Alqahtani | João Sedoc | Roman Klinger | Alexandra Balahur
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

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WASSA 2022 Shared Task: Predicting Empathy, Emotion and Personality in Reaction to News Stories
Valentin Barriere | Shabnam Tafreshi | João Sedoc | Sawsan Alqahtani
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

This paper presents the results that were obtained from WASSA 2022 shared task on predicting empathy, emotion, and personality in reaction to news stories. 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 and Batson’s empathic concern and personal distress scores. The dataset was further extended in WASSA 2021 shared task to include news articles, person-level demographic information (e.g. age, gender), personality information, and Ekman’s six basic emotions at essay level Participation was encouraged in four tracks: predicting empathy and distress scores, predicting emotion categories, predicting personality and predicting interpersonal reactivity. In total, 14 teams participated in the shared task. We summarize the methods and resources used by the participating teams.

2021

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Proceedings of the Second Workshop on Insights from Negative Results in NLP
João Sedoc | Anna Rogers | Anna Rumshisky | Shabnam Tafreshi
Proceedings of the Second Workshop on Insights from Negative Results in NLP

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Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Orphee De Clercq | Alexandra Balahur | Joao Sedoc | Valentin Barriere | Shabnam Tafreshi | Sven Buechel | Veronique Hoste
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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WASSA 2021 Shared Task: Predicting Empathy and Emotion in Reaction to News Stories
Shabnam Tafreshi | Orphee De Clercq | Valentin Barriere | Sven Buechel | João Sedoc | Alexandra Balahur
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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.

2019

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GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus
Shabnam Tafreshi | Mona Diab
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : EmoContext. Our approach is a Gated Recurrent Neural Network (GRU) model with attention layer is bootstrapped with contextual information and trained with a multigenre corpus, which is combination of several popular emotional data sets. We utilize different word embeddings to empirically select the most suited embedding to represent our features. Our aim is to build a robust emotion classifier that can generalize emotion detection, which is to learn emotion cues in a noisy training environment. To fulfill this aim we train our model with a multigenre emotion corpus, this way we leverage from having more training set. We achieved overall %56.05 f1-score and placed 144. Given our aim and noisy training environment, the results are anticipated.

2018

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Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus
Shabnam Tafreshi | Mona Diab
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning
Shabnam Tafreshi | Mona Diab
Proceedings of the 27th International Conference on Computational Linguistics

Detection and classification of emotion categories expressed by a sentence is a challenging task due to subjectivity of emotion. To date, most of the models are trained and evaluated on single genre and when used to predict emotion in different genre their performance drops by a large margin. To address the issue of robustness, we model the problem within a joint multi-task learning framework. We train this model with a multigenre emotion corpus to predict emotions across various genre. Each genre is represented as a separate task, we use soft parameter shared layers across the various tasks. our experimental results show that this model improves the results across the various genres, compared to a single genre training in the same neural net architecture.