@inproceedings{lee-etal-2019-milab,
title = "{MILAB} at {S}em{E}val-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis",
author = "Lee, Yoonhyung and
Kim, Yanghoon and
Jung, Kyomin",
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/S19-2043",
doi = "10.18653/v1/S19-2043",
pages = "256--260",
abstract = "This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue. To address this challenge of predicting the emotion considering its context, we propose a Multi-View Turn-by-Turn (MVTT) model. Firstly, MVTT model generates vectors from each utterance using two encoders: word-level Bi-GRU encoder (WLE) and character-level CNN encoder (CLE). Then, MVTT grasps contextual information by combining the vectors and predict the emotion with the contextual information. We conduct experiments on the effect of vector encoding and vector combination. Our final MVTT model achieved 0.7634 microaveraged F1 score.",
}
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<abstract>This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue. To address this challenge of predicting the emotion considering its context, we propose a Multi-View Turn-by-Turn (MVTT) model. Firstly, MVTT model generates vectors from each utterance using two encoders: word-level Bi-GRU encoder (WLE) and character-level CNN encoder (CLE). Then, MVTT grasps contextual information by combining the vectors and predict the emotion with the contextual information. We conduct experiments on the effect of vector encoding and vector combination. Our final MVTT model achieved 0.7634 microaveraged F1 score.</abstract>
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%0 Conference Proceedings
%T MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis
%A Lee, Yoonhyung
%A Kim, Yanghoon
%A Jung, Kyomin
%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 lee-etal-2019-milab
%X This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue. To address this challenge of predicting the emotion considering its context, we propose a Multi-View Turn-by-Turn (MVTT) model. Firstly, MVTT model generates vectors from each utterance using two encoders: word-level Bi-GRU encoder (WLE) and character-level CNN encoder (CLE). Then, MVTT grasps contextual information by combining the vectors and predict the emotion with the contextual information. We conduct experiments on the effect of vector encoding and vector combination. Our final MVTT model achieved 0.7634 microaveraged F1 score.
%R 10.18653/v1/S19-2043
%U https://aclanthology.org/S19-2043
%U https://doi.org/10.18653/v1/S19-2043
%P 256-260
Markdown (Informal)
[MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis](https://aclanthology.org/S19-2043) (Lee et al., SemEval 2019)
ACL