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    <abstract>Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dialogic, i.e., news and journals. This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue. In this paper, we designed a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems. We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.</abstract>
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%0 Conference Proceedings
%T Implicit Discourse Relation Identification for Open-domain Dialogues
%A Ma, Mingyu Derek
%A Bowden, Kevin
%A Wu, Jiaqi
%A Cui, Wen
%A Walker, Marilyn
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ma-etal-2019-implicit
%X Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dialogic, i.e., news and journals. This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue. In this paper, we designed a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems. We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.
%R 10.18653/v1/P19-1065
%U https://aclanthology.org/P19-1065
%U https://doi.org/10.18653/v1/P19-1065
%P 666-672

Markdown (Informal)

[Implicit Discourse Relation Identification for Open-domain Dialogues](https://aclanthology.org/P19-1065) (Ma et al., ACL 2019)

• Implicit Discourse Relation Identification for Open-domain Dialogues (Ma et al., ACL 2019)

ACL

• Mingyu Derek Ma, Kevin Bowden, Jiaqi Wu, Wen Cui, and Marilyn Walker. 2019. Implicit Discourse Relation Identification for Open-domain Dialogues. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 666–672, Florence, Italy. Association for Computational Linguistics.