@inproceedings{tseng-etal-2021-cread,
    title = "{CREAD}: Combined Resolution of Ellipses and Anaphora in Dialogues",
    author = "Tseng, Bo-Hsiang  and
      Bhargava, Shruti  and
      Lu, Jiarui  and
      Moniz, Joel Ruben Antony  and
      Piraviperumal, Dhivya  and
      Li, Lin  and
      Yu, Hong",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.265",
    doi = "10.18653/v1/2021.naacl-main.265",
    pages = "3390--3406",
    abstract = "Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a self-contained rewritten user query. To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries. Results show that the performance of query rewrite can be substantially boosted (+2.3{\%} F1) with the aid of coreference modeling. Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2{\%} F1) on this dataset.",
}