@inproceedings{li-etal-2023-well,
    title = "How Well Apply Simple {MLP} to Incomplete Utterance Rewriting?",
    author = "Li, Jiang  and
      Su, Xiangdong  and
      Ma, Xinlan  and
      Gao, Guanglai",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.134",
    doi = "10.18653/v1/2023.acl-short.134",
    pages = "1567--1576",
    abstract = "Incomplete utterance rewriting (IUR) aims to restore the incomplete utterance with sufficient context information for comprehension. This paper introduces a simple yet efficient IUR method. Different from prior studies, we first employ only one-layer \textbf{M}LP architecture to mine latent semantic information between joint utterances for \textbf{IUR} task (\textbf{MIUR}). After that, we conduct a joint feature matrix to predict the token type and thus restore the incomplete utterance. The well-designed network and simple architecture make our method significantly superior to existing methods in terms of quality and inference speedOur code is available at \url{https://github.com/IMU-MachineLearningSXD/MIUR}.",
}

Incomplete utterance rewriting (IUR) aims to restore the incomplete utterance with sufficient context information for comprehension. This paper introduces a simple yet efficient IUR method. Different from prior studies, we first employ only one-layer \textbf{M}LP architecture to mine latent semantic information between joint utterances for \textbf{IUR} task (\textbf{MIUR}). After that, we conduct a joint feature matrix to predict the token type and thus restore the incomplete utterance. The well-designed network and simple architecture make our method significantly superior to existing methods in terms of quality and inference speedOur code is available at \url{https://github.com/IMU-MachineLearningSXD/MIUR}.
Li, Jiang Su, Xiangdong Ma, Xinlan Gao, Guanglai
How Well Apply Simple MLP to Incomplete Utterance Rewriting?