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.- Anthology ID:
- 2023.acl-short.134
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1567–1576
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.134
- DOI:
- 10.18653/v1/2023.acl-short.134
- Cite (ACL):
- Jiang Li, Xiangdong Su, Xinlan Ma, and Guanglai Gao. 2023. How Well Apply Simple MLP to Incomplete Utterance Rewriting?. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1567–1576, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- How Well Apply Simple MLP to Incomplete Utterance Rewriting? (Li et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.acl-short.134.pdf