@inproceedings{tucker-etal-2022-syntax,
title = "When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes",
author = "Tucker, Mycal and
Eisape, Tiwalayo and
Qian, Peng and
Levy, Roger and
Shah, Julie",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.394",
doi = "10.18653/v1/2022.naacl-main.394",
pages = "5393--5408",
abstract = "Recent causal probing literature reveals when language models and syntactic probes use similar representations. Such techniques may yield {``}false negative{''} causality results: models may use representations of syntax, but probes may have learned to use redundant encodings of the same syntactic information. We demonstrate that models do encode syntactic information redundantly and introduce a new probe design that guides probes to consider all syntactic information present in embeddings. Using these probes, we find evidence for the use of syntax in models where prior methods did not, allowing us to boost model performance by injecting syntactic information into representations.",
}
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<abstract>Recent causal probing literature reveals when language models and syntactic probes use similar representations. Such techniques may yield “false negative” causality results: models may use representations of syntax, but probes may have learned to use redundant encodings of the same syntactic information. We demonstrate that models do encode syntactic information redundantly and introduce a new probe design that guides probes to consider all syntactic information present in embeddings. Using these probes, we find evidence for the use of syntax in models where prior methods did not, allowing us to boost model performance by injecting syntactic information into representations.</abstract>
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%0 Conference Proceedings
%T When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes
%A Tucker, Mycal
%A Eisape, Tiwalayo
%A Qian, Peng
%A Levy, Roger
%A Shah, Julie
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F tucker-etal-2022-syntax
%X Recent causal probing literature reveals when language models and syntactic probes use similar representations. Such techniques may yield “false negative” causality results: models may use representations of syntax, but probes may have learned to use redundant encodings of the same syntactic information. We demonstrate that models do encode syntactic information redundantly and introduce a new probe design that guides probes to consider all syntactic information present in embeddings. Using these probes, we find evidence for the use of syntax in models where prior methods did not, allowing us to boost model performance by injecting syntactic information into representations.
%R 10.18653/v1/2022.naacl-main.394
%U https://aclanthology.org/2022.naacl-main.394
%U https://doi.org/10.18653/v1/2022.naacl-main.394
%P 5393-5408
Markdown (Informal)
[When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes](https://aclanthology.org/2022.naacl-main.394) (Tucker et al., NAACL 2022)
ACL