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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

CoAStaL at SemEval-2019 Task 3 : Affect Classification in Dialogue using Attentive BiLSTMs. / González, Ana Valeria; Petrén Bach Hansen, Victor; Bingel, Joachim; Søgaard, Anders.

Proceedings of the 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics, 2019. s. 169-174.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

González, AV, Petrén Bach Hansen, V, Bingel, J & Søgaard, A 2019, CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs. i Proceedings of the 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics, s. 169-174, 13th International Workshop on Semantic Evaluation (SemEval-2019), Minneapolis, USA, 06/06/2019. https://doi.org/10.18653/v1/S19-2026

APA

González, A. V., Petrén Bach Hansen, V., Bingel, J., & Søgaard, A. (2019). CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs. I Proceedings of the 13th International Workshop on Semantic Evaluation (s. 169-174). Association for Computational Linguistics. https://doi.org/10.18653/v1/S19-2026

Vancouver

González AV, Petrén Bach Hansen V, Bingel J, Søgaard A. CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs. I Proceedings of the 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics. 2019. s. 169-174 https://doi.org/10.18653/v1/S19-2026

Author

González, Ana Valeria ; Petrén Bach Hansen, Victor ; Bingel, Joachim ; Søgaard, Anders. / CoAStaL at SemEval-2019 Task 3 : Affect Classification in Dialogue using Attentive BiLSTMs. Proceedings of the 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics, 2019. s. 169-174

Bibtex

@inproceedings{62d88b811d3c41948b07bdf6e1911eb9,
title = "CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs",
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.",
author = "Gonz{\'a}lez, {Ana Valeria} and {Petr{\'e}n Bach Hansen}, Victor and Joachim Bingel and Anders S{\o}gaard",
year = "2019",
doi = "10.18653/v1/S19-2026",
language = "English",
pages = "169--174",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
publisher = "Association for Computational Linguistics",
note = "null ; Conference date: 06-06-2019 Through 07-06-2019",

}

RIS

TY - GEN

T1 - CoAStaL at SemEval-2019 Task 3

AU - González, Ana Valeria

AU - Petrén Bach Hansen, Victor

AU - Bingel, Joachim

AU - Søgaard, Anders

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.18653/v1/S19-2026

DO - 10.18653/v1/S19-2026

M3 - Article in proceedings

SP - 169

EP - 174

BT - Proceedings of the 13th International Workshop on Semantic Evaluation

PB - Association for Computational Linguistics

Y2 - 6 June 2019 through 7 June 2019

ER -

ID: 240420498