@inproceedings{poelman-etal-2022-transparent,
title = "Transparent Semantic Parsing with {U}niversal {D}ependencies Using Graph Transformations",
author = "Poelman, Wessel and
van Noord, Rik and
Bos, Johan",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.367",
pages = "4186--4192",
abstract = "Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability. Taking advantage of existing off-the-shelf Universal Dependency parsers, we present a method that maps a syntactic dependency tree to a formal meaning representation based on Discourse Representation Theory. Rather than using lambda calculus to manage variable bindings, our approach is novel in that it consists of using a series of graph transformations. The resulting UD semantic parser shows good performance for English, German, Italian and Dutch, with F-scores over 75{\%}, outperforming a neural semantic parser for the lower-resourced languages. Unlike neural semantic parsers, our UD semantic parser does not hallucinate output, is relatively easy to port to other languages, and is completely transparent.",
}
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%0 Conference Proceedings
%T Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations
%A Poelman, Wessel
%A van Noord, Rik
%A Bos, Johan
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F poelman-etal-2022-transparent
%X Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability. Taking advantage of existing off-the-shelf Universal Dependency parsers, we present a method that maps a syntactic dependency tree to a formal meaning representation based on Discourse Representation Theory. Rather than using lambda calculus to manage variable bindings, our approach is novel in that it consists of using a series of graph transformations. The resulting UD semantic parser shows good performance for English, German, Italian and Dutch, with F-scores over 75%, outperforming a neural semantic parser for the lower-resourced languages. Unlike neural semantic parsers, our UD semantic parser does not hallucinate output, is relatively easy to port to other languages, and is completely transparent.
%U https://aclanthology.org/2022.coling-1.367
%P 4186-4192
Markdown (Informal)
[Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations](https://aclanthology.org/2022.coling-1.367) (Poelman et al., COLING 2022)
ACL