About Time: Do Transformers Learn Temporal Verbal Aspect?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11210%2F22%3A10456696" target="_blank" >RIV/00216208:11210/22:10456696 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.18653/v1/2022.cmcl-1.10" target="_blank" >https://doi.org/10.18653/v1/2022.cmcl-1.10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.cmcl-1.10" target="_blank" >10.18653/v1/2022.cmcl-1.10</a>
Alternative languages
Result language
angličtina
Original language name
About Time: Do Transformers Learn Temporal Verbal Aspect?
Original language description
Aspect is a linguistic concept that describes how an action, event, or state of a verb phrase is situated in time. In this paper, we explore whether different transformer models are capable of identifying aspectual features. We focus on two specific aspectual features: telicity and duration. Telicity marks whether the verb's action or state has an endpoint or not (telic/atelic), and duration denotes whether a verb expresses an action (dynamic) or a state (stative). These features are integral to the interpretation of natural language, but also hard to annotate and identify with NLP methods. Our results show that transformer models adequately capture information on telicity and duration in their vectors, even in their pretrained forms, but are somewhat biased with regard to verb tense and word order.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
60203 - Linguistics
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