@inproceedings{thukral-etal-2021-probing,
title = "Probing Language Models for Understanding of Temporal Expressions",
author = "Thukral, Shivin and
Kukreja, Kunal and
Kavouras, Christian",
editor = "Bastings, Jasmijn and
Belinkov, Yonatan and
Dupoux, Emmanuel and
Giulianelli, Mario and
Hupkes, Dieuwke and
Pinter, Yuval and
Sajjad, Hassan",
booktitle = "Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.blackboxnlp-1.31/",
doi = "10.18653/v1/2021.blackboxnlp-1.31",
pages = "396--406",
abstract = "We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions. More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units. We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions."
}
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<abstract>We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions. More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units. We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions.</abstract>
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%0 Conference Proceedings
%T Probing Language Models for Understanding of Temporal Expressions
%A Thukral, Shivin
%A Kukreja, Kunal
%A Kavouras, Christian
%Y Bastings, Jasmijn
%Y Belinkov, Yonatan
%Y Dupoux, Emmanuel
%Y Giulianelli, Mario
%Y Hupkes, Dieuwke
%Y Pinter, Yuval
%Y Sajjad, Hassan
%S Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F thukral-etal-2021-probing
%X We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions. More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units. We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions.
%R 10.18653/v1/2021.blackboxnlp-1.31
%U https://aclanthology.org/2021.blackboxnlp-1.31/
%U https://doi.org/10.18653/v1/2021.blackboxnlp-1.31
%P 396-406
Markdown (Informal)
[Probing Language Models for Understanding of Temporal Expressions](https://aclanthology.org/2021.blackboxnlp-1.31/) (Thukral et al., BlackboxNLP 2021)
ACL