@inproceedings{he-etal-2021-speaker-turn,
title = "Speaker Turn Modeling for Dialogue Act Classification",
author = "He, Zihao and
Tavabi, Leili and
Lerman, Kristina and
Soleymani, Mohammad",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.185",
doi = "10.18653/v1/2021.findings-emnlp.185",
pages = "2150--2157",
abstract = "Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive written text. In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs. Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification. With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation. Validation on three benchmark public datasets demonstrates superior performance of our model.",
}
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<abstract>Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive written text. In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs. Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification. With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation. Validation on three benchmark public datasets demonstrates superior performance of our model.</abstract>
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%0 Conference Proceedings
%T Speaker Turn Modeling for Dialogue Act Classification
%A He, Zihao
%A Tavabi, Leili
%A Lerman, Kristina
%A Soleymani, Mohammad
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F he-etal-2021-speaker-turn
%X Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive written text. In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs. Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification. With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation. Validation on three benchmark public datasets demonstrates superior performance of our model.
%R 10.18653/v1/2021.findings-emnlp.185
%U https://aclanthology.org/2021.findings-emnlp.185
%U https://doi.org/10.18653/v1/2021.findings-emnlp.185
%P 2150-2157
Markdown (Informal)
[Speaker Turn Modeling for Dialogue Act Classification](https://aclanthology.org/2021.findings-emnlp.185) (He et al., Findings 2021)
ACL
- Zihao He, Leili Tavabi, Kristina Lerman, and Mohammad Soleymani. 2021. Speaker Turn Modeling for Dialogue Act Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2150–2157, Punta Cana, Dominican Republic. Association for Computational Linguistics.