Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph J. Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros C Polymenakos, Walter Lasecki
| Challenge: | a dataset of 77,563 messages manually annotated with reply-structure graphs disentangles conversations and defines internal conversation structure. |
| Approach: | They use a dataset of 77,563 messages manually annotated with reply-structure graphs to disentangle conversations and define internal conversation structure. |
| Outcome: | The new dataset is 16 times larger than all previous datasets combined and includes adjudication of annotation disagreements and context. |
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| Challenge: | Existing methods focus on pairwise utterance relations but pay inadequate attention to utterant-to-context relation modeling. |
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Unsupervised Conversation Disentanglement through Co-Training (2021.emnlp-main)
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| Challenge: | Existing work on conversation disentanglement relies heavily on human annotations, which is expensive to obtain in practice. |
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| Challenge: | Conversations are the natural communication format for people. |
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More Diverse Dialogue Datasets via Diversity-Informed Data Collection (2020.acl-main)
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SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (D19-54)
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