CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


This work describes the system presented by the CoAStaL Natural Language Processing group at University of Copenhagen. The main system we present uses the same attention mechanism presented in (Yang et al., 2016). Our overall model architecture is also inspired by their hierarchical classification model and adapted to deal with classification in dialogue by encoding information at the turn level. We use different encodings for each turn to create a more expressive representation of dialogue context which is then fed into our classifier.We also define a custom preprocessing step in order to deal with language commonly used in interactions across many social media outlets. Our proposed system achieves a micro F1 score of 0.7340 on the test set and shows significant gains in performance compared to a system using dialogue level encoding.
Original languageEnglish
Title of host publicationProceedings of the 13th International Workshop on Semantic Evaluation
PublisherAssociation for Computational Linguistics
Publication date2019
Publication statusPublished - 2019
Event13th International Workshop on Semantic Evaluation (SemEval-2019) - Minneapolis, United States
Duration: 6 Jun 20197 Jun 2019


Conference13th International Workshop on Semantic Evaluation (SemEval-2019)
LandUnited States

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