@inproceedings{du-etal-2023-multi,
    title = "Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting",
    author = "Du, Haowei  and
      Zhang, Dinghao  and
      Li, Chen  and
      Li, Yang  and
      Zhao, Dongyan",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.findings-emnlp.168/",
    doi = "10.18653/v1/2023.findings-emnlp.168",
    pages = "2576--2581",
    abstract = "Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances. We propose a novel and effective multi-task information interaction framework including context selection, edit matrix construction, and relevance merging to capture the multi-granularity of semantic information. Benefiting from fetching the relevant utterance and figuring out the important words, our approach outperforms existing state-of-the-art models on two benchmark datasets Restoration-200K and CANAND in this field."
}