@inproceedings{gonzalez-etal-2019-coastal,
title = "{C}o{AS}ta{L} at {S}em{E}val-2019 Task 3: Affect Classification in Dialogue using Attentive {B}i{LSTM}s",
author = "Gonz{\'a}lez, Ana Valeria and
Petr{\'e}n Bach Hansen, Victor and
Bingel, Joachim and
S{\o}gaard, Anders",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2026",
doi = "10.18653/v1/S19-2026",
pages = "169--174",
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.",
}
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%0 Conference Proceedings
%T CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs
%A González, Ana Valeria
%A Petrén Bach Hansen, Victor
%A Bingel, Joachim
%A Søgaard, Anders
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F gonzalez-etal-2019-coastal
%X 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.
%R 10.18653/v1/S19-2026
%U https://aclanthology.org/S19-2026
%U https://doi.org/10.18653/v1/S19-2026
%P 169-174
Markdown (Informal)
[CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs](https://aclanthology.org/S19-2026) (González et al., SemEval 2019)
ACL