@inproceedings{yamaki-etal-2023-holographic,
title = "Holographic {CCG} Parsing",
author = "Yamaki, Ryosuke and
Taniguchi, Tadahiro and
Mochihashi, Daichi",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.15",
doi = "10.18653/v1/2023.acl-long.15",
pages = "262--276",
abstract = "We propose a method for formulating CCG as a recursive composition in a continuous vector space. Recent CCG supertagging and parsing models generally demonstrate high performance, yet rely on black-box neural architectures to implicitly model phrase structure dependencies. Instead, we leverage the method of holographic embeddings as a compositional operator to explicitly model the dependencies between words and phrase structures in the embedding space. Experimental results revealed that holographic composition effectively improves the supertagging accuracy to achieve state-of-the-art parsing performance when using a C{\&}C parser. The proposed span-based parsing algorithm using holographic composition achieves performance comparable to state-of-the-art neural parsing with Transformers. Furthermore, our model can semantically and syntactically infill text at the phrase level due to the decomposability of holographic composition.",
}
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%0 Conference Proceedings
%T Holographic CCG Parsing
%A Yamaki, Ryosuke
%A Taniguchi, Tadahiro
%A Mochihashi, Daichi
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yamaki-etal-2023-holographic
%X We propose a method for formulating CCG as a recursive composition in a continuous vector space. Recent CCG supertagging and parsing models generally demonstrate high performance, yet rely on black-box neural architectures to implicitly model phrase structure dependencies. Instead, we leverage the method of holographic embeddings as a compositional operator to explicitly model the dependencies between words and phrase structures in the embedding space. Experimental results revealed that holographic composition effectively improves the supertagging accuracy to achieve state-of-the-art parsing performance when using a C&C parser. The proposed span-based parsing algorithm using holographic composition achieves performance comparable to state-of-the-art neural parsing with Transformers. Furthermore, our model can semantically and syntactically infill text at the phrase level due to the decomposability of holographic composition.
%R 10.18653/v1/2023.acl-long.15
%U https://aclanthology.org/2023.acl-long.15
%U https://doi.org/10.18653/v1/2023.acl-long.15
%P 262-276
Markdown (Informal)
[Holographic CCG Parsing](https://aclanthology.org/2023.acl-long.15) (Yamaki et al., ACL 2023)
ACL
- Ryosuke Yamaki, Tadahiro Taniguchi, and Daichi Mochihashi. 2023. Holographic CCG Parsing. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 262–276, Toronto, Canada. Association for Computational Linguistics.