Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AD9D7L695" target="_blank" >RIV/00216208:11320/22:D9D7L695 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.lchange-1.6" target="_blank" >https://aclanthology.org/2022.lchange-1.6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.lchange-1.6" target="_blank" >10.18653/v1/2022.lchange-1.6</a>
Alternative languages
Result language
angličtina
Original language name
Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change
Original language description
Morphological and syntactic changes in word usage — as captured, e.g., by grammatical profiles — have been shown to be good predictors of a word's meaning change. In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes. To this end, we first compare the performance of grammatical profiles against that of a multilingual neural language model (XLM-R) on 10 datasets, covering 7 languages, and then combine the two approaches in ensembles to assess their complementarity. Our results show that ensembling grammatical profiles with XLM-R improves semantic change detection performance for most datasets and languages. This indicates that language models do not fully cover the fine-grained morphological and syntactic signals that are explicitly represented in grammatical profiles. An interesting exception are the test sets where the time spans under analysis are much longer than the time gap between them (for example, century-long spans with a one-year gap between them). Morphosyntactic change is slow so grammatical profiles do not detect in such cases. In contrast, language models, thanks to their access to lexical information, are able to detect fast topical changes.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change
ISBN
978-1-955917-42-1
ISSN
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e-ISSN
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Number of pages
14
Pages from-to
54-67
Publisher name
Association for Computational Linguistics
Place of publication
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Event location
Dublin, Ireland
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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