Abstract
We tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities. We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified. The proposed method achieves efficient alignment by mapping forests on a packed structure. The experimental results indicated that our method improves the phrase alignment quality of the state-of-the-art method by aligning forests rather than 1-best trees.- Anthology ID:
- 2023.starsem-1.39
- Volume:
- Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Alexis Palmer, Jose Camacho-collados
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 449–455
- Language:
- URL:
- https://aclanthology.org/2023.starsem-1.39
- DOI:
- 10.18653/v1/2023.starsem-1.39
- Cite (ACL):
- Sora Kadotani and Yuki Arase. 2023. Monolingual Phrase Alignment as Parse Forest Mapping. In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), pages 449–455, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Monolingual Phrase Alignment as Parse Forest Mapping (Kadotani & Arase, *SEM 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2023.starsem-1.39.pdf