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

Ana Valeria González, Victor Petrén Bach Hansen, Joachim Bingel, Anders Søgaard


Abstract
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.
Anthology ID:
S19-2026
Original:
S19-2026v1
Version 2:
S19-2026v2
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
169–174
Language:
URL:
https://aclanthology.org/S19-2026
DOI:
10.18653/v1/S19-2026
Bibkey:
Cite (ACL):
Ana Valeria González, Victor Petrén Bach Hansen, Joachim Bingel, and Anders Søgaard. 2019. CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 169–174, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs (González et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2026.pdf
Data
EmoContext