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    <abstract>Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.</abstract>
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%0 Conference Proceedings
%T Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
%A Le, Ran
%A Hu, Wenpeng
%A Shang, Mingyue
%A You, Zhenjun
%A Bing, Lidong
%A Zhao, Dongyan
%A Yan, Rui
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F le-etal-2019-speaking
%X Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.
%R 10.18653/v1/D19-1199
%U https://aclanthology.org/D19-1199
%U https://doi.org/10.18653/v1/D19-1199
%P 1909-1919

Markdown (Informal)

[Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations](https://aclanthology.org/D19-1199) (Le et al., EMNLP-IJCNLP 2019)

• Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (Le et al., EMNLP-IJCNLP 2019)

ACL

• Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, and Rui Yan. 2019. Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1909–1919, Hong Kong, China. Association for Computational Linguistics.