@inproceedings{wijnholds-moortgat-2023-structural,
title = "Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of {D}utch Clause Relativization",
author = "Wijnholds, Gijs and
Moortgat, Michael",
editor = "Jiang, Jing and
Reitter, David and
Deng, Shumin",
booktitle = "Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.conll-1.11",
doi = "10.18653/v1/2023.conll-1.11",
pages = "155--164",
abstract = "This paper addresses structural ambiguity in Dutch relative clauses. By investigating the task of disambiguation by grounding, we study how the presence of a prior sentence can resolve relative clause ambiguities. We apply this method to two parsing architectures in an attempt to demystify the parsing and language model components of two present-day neural parsers. Results show that a neurosymbolic parser, based on proof nets, is more open to data bias correction than an approach based on universal dependencies, although both set-ups suffer from a comparable initial data bias.",
}
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%0 Conference Proceedings
%T Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization
%A Wijnholds, Gijs
%A Moortgat, Michael
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wijnholds-moortgat-2023-structural
%X This paper addresses structural ambiguity in Dutch relative clauses. By investigating the task of disambiguation by grounding, we study how the presence of a prior sentence can resolve relative clause ambiguities. We apply this method to two parsing architectures in an attempt to demystify the parsing and language model components of two present-day neural parsers. Results show that a neurosymbolic parser, based on proof nets, is more open to data bias correction than an approach based on universal dependencies, although both set-ups suffer from a comparable initial data bias.
%R 10.18653/v1/2023.conll-1.11
%U https://aclanthology.org/2023.conll-1.11
%U https://doi.org/10.18653/v1/2023.conll-1.11
%P 155-164
Markdown (Informal)
[Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization](https://aclanthology.org/2023.conll-1.11) (Wijnholds & Moortgat, CoNLL 2023)
ACL