@inproceedings{mohiuddin-etal-2019-adaptation,
title = "Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation",
author = "Mohiuddin, Tasnim and
Nguyen, Thanh-Tung and
Joty, Shafiq",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1134",
doi = "10.18653/v1/N19-1134",
pages = "1326--1336",
abstract = "We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled conversational data and the available labeled data from synchronous domains. We carry out our research in three main steps. First, we introduce a neural architecture based on hierarchical LSTMs and conditional random fields (CRF) for SAR, and show that our method outperforms existing methods when trained on in-domain data only. Second, we improve our initial SAR models by semi-supervised learning in the form of pretrained word embeddings learned from a large unlabeled conversational corpus. Finally, we employ adversarial training to improve the results further by leveraging the labeled data from synchronous domains and by explicitly modeling the distributional shift in two domains.",
}
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%0 Conference Proceedings
%T Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation
%A Mohiuddin, Tasnim
%A Nguyen, Thanh-Tung
%A Joty, Shafiq
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F mohiuddin-etal-2019-adaptation
%X We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled conversational data and the available labeled data from synchronous domains. We carry out our research in three main steps. First, we introduce a neural architecture based on hierarchical LSTMs and conditional random fields (CRF) for SAR, and show that our method outperforms existing methods when trained on in-domain data only. Second, we improve our initial SAR models by semi-supervised learning in the form of pretrained word embeddings learned from a large unlabeled conversational corpus. Finally, we employ adversarial training to improve the results further by leveraging the labeled data from synchronous domains and by explicitly modeling the distributional shift in two domains.
%R 10.18653/v1/N19-1134
%U https://aclanthology.org/N19-1134
%U https://doi.org/10.18653/v1/N19-1134
%P 1326-1336
Markdown (Informal)
[Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation](https://aclanthology.org/N19-1134) (Mohiuddin et al., NAACL 2019)
ACL