Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

Zhu, Henghui; Feng, Nan; Wang, Zhiguo; Nallapati, Ramesh; Xiang, Bing

doi:10.48550/arxiv.1911.10666

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Kummerfeld et al (2019) uses feed-forward networks with averaged pre-trained word embedding and many hand-engineered features. Tan et al (2019) used an utterance-level LSTM network, while Zhu et al (2019) used a masked transformer to get a contextaware utterance representation considering utterances in the same conversation.…”

Section: Conversation Disentanglementmentioning

confidence: 99%

Online Conversation Disentanglement with Pointer Networks

Yu¹,

Joty²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Huge amounts of textual conversations occur online every day, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. However, existing disentanglement methods rely mostly on handcrafted features that are dataset specific, which hinders generalization and adaptability. In this work, we propose an end-to-end online framework for conversation disentanglement that avoids time-consuming domain-specific feature engineering. We design a novel way to embed the whole utterance that comprises timestamp, speaker, and message text, and propose a custom attention mechanism that models disentanglement as a pointing problem while effectively capturing inter-utterance interactions in an end-to-end fashion. We also introduce a joint-learning objective to better capture contextual information. Our experiments on the Ubuntu IRC dataset show that our method achieves state-of-the-art performance in both link and conversation prediction tasks.

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Section: Conversation Disentanglementmentioning

confidence: 99%

Online Conversation Disentanglement with Pointer Networks

Yu¹,

Joty²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Chen

Yang

2020

Preprint

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Text summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing conversations-an essential part of humanhuman/machine interaction where most important pieces of information are scattered across various utterances of different speakersremains relatively under-investigated. This work proposes a multi-view sequence-tosequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment. We also discussed specific challenges that current approaches faced with this task. We have publicly released our code at https://github.com/GT-SALT/ Multi-View-Seq2Seq.

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Response Selection for Multi-Party Conversations with Dynamic Topic Tracking

Wang

Joty

Hoi

2020

Preprint

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While participants in a multi-party multi-turn conversation simultaneously engage in multiple conversation topics, existing response selection methods are developed mainly focusing on a two-party single-conversation scenario. Hence, the prolongation and transition of conversation topics are ignored by current methods. In this work, we frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. With this new formulation, we propose a novel multi-task learning framework that supports efficient encoding through large pretrained models with only two utterances at once to perform dynamic topic disentanglement and response selection. We also propose Topic-BERT an essential pretraining step to embed topic information into BERT with self-supervised learning. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks outperforming existing methods by a good margin. 1

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Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

Cited by 3 publications

References 15 publications

Online Conversation Disentanglement with Pointer Networks

Online Conversation Disentanglement with Pointer Networks

Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

Response Selection for Multi-Party Conversations with Dynamic Topic Tracking

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