How Well Apply Simple MLP to Incomplete Utterance Rewriting?

Jiang Li; Xiangdong Su; Xinlan Ma; Guanglai Gao

ACL 2023short5 citations

How Well Apply Simple MLP to Incomplete Utterance Rewriting?

Jiang Li, Xiangdong Su, Xinlan Ma, Guanglai Gao

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 MLP architecture to mine latent semantic information between joint utterances for IUR task (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 https://github.com/IMU-MachineLearningSXD/MIUR.

BibTeX

@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"
}