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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Dokumenter

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.
OriginalsprogEngelsk
TitelProceedings of the 13th International Workshop on Semantic Evaluation
ForlagAssociation for Computational Linguistics
Publikationsdato2019
Sider169-174
DOI
StatusUdgivet - 2019
Begivenhed13th International Workshop on Semantic Evaluation (SemEval-2019) - Minneapolis, USA
Varighed: 6 jun. 20197 jun. 2019

Konference

Konference13th International Workshop on Semantic Evaluation (SemEval-2019)
LandUSA
ByMinneapolis
Periode06/06/201907/06/2019

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