@inproceedings{kadotani-arase-2023-monolingual,
title = "Monolingual Phrase Alignment as Parse Forest Mapping",
author = "Kadotani, Sora and
Arase, Yuki",
editor = "Palmer, Alexis and
Camacho-collados, Jose",
booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.starsem-1.39",
doi = "10.18653/v1/2023.starsem-1.39",
pages = "449--455",
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.",
}
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%0 Conference Proceedings
%T Monolingual Phrase Alignment as Parse Forest Mapping
%A Kadotani, Sora
%A Arase, Yuki
%Y Palmer, Alexis
%Y Camacho-collados, Jose
%S Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kadotani-arase-2023-monolingual
%X 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.
%R 10.18653/v1/2023.starsem-1.39
%U https://aclanthology.org/2023.starsem-1.39
%U https://doi.org/10.18653/v1/2023.starsem-1.39
%P 449-455
Markdown (Informal)
[Monolingual Phrase Alignment as Parse Forest Mapping](https://aclanthology.org/2023.starsem-1.39) (Kadotani & Arase, *SEM 2023)
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.