@inproceedings{chen-palmer-2022-contrast,
title = "Contrast Sets for Stativity of {E}nglish Verbs in Context",
author = "Chen, Daniel and
Palmer, Alexis",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.354/",
pages = "4028--4036",
abstract = "For the task of classifying verbs in context as dynamic or stative, current models approach human performance, but only for particular data sets. To better understand the performance of such models, and how well they are able to generalize beyond particular test sets, we apply the contrast set (Gardner et al., 2020) methodology to stativity classification. We create nearly 300 contrastive pairs by perturbing test set instances just enough to change their labels from one class to the other, while preserving coherence, meaning, and well-formedness. Contrastive evaluation shows that a model with near-human performance on an in-distribution test set degrades substantially when applied to transformed examples, showing that the stative vs. dynamic classification task is more complex than the model performance might otherwise suggest. Code and data are freely available."
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<abstract>For the task of classifying verbs in context as dynamic or stative, current models approach human performance, but only for particular data sets. To better understand the performance of such models, and how well they are able to generalize beyond particular test sets, we apply the contrast set (Gardner et al., 2020) methodology to stativity classification. We create nearly 300 contrastive pairs by perturbing test set instances just enough to change their labels from one class to the other, while preserving coherence, meaning, and well-formedness. Contrastive evaluation shows that a model with near-human performance on an in-distribution test set degrades substantially when applied to transformed examples, showing that the stative vs. dynamic classification task is more complex than the model performance might otherwise suggest. Code and data are freely available.</abstract>
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%0 Conference Proceedings
%T Contrast Sets for Stativity of English Verbs in Context
%A Chen, Daniel
%A Palmer, Alexis
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F chen-palmer-2022-contrast
%X For the task of classifying verbs in context as dynamic or stative, current models approach human performance, but only for particular data sets. To better understand the performance of such models, and how well they are able to generalize beyond particular test sets, we apply the contrast set (Gardner et al., 2020) methodology to stativity classification. We create nearly 300 contrastive pairs by perturbing test set instances just enough to change their labels from one class to the other, while preserving coherence, meaning, and well-formedness. Contrastive evaluation shows that a model with near-human performance on an in-distribution test set degrades substantially when applied to transformed examples, showing that the stative vs. dynamic classification task is more complex than the model performance might otherwise suggest. Code and data are freely available.
%U https://aclanthology.org/2022.coling-1.354/
%P 4028-4036
Markdown (Informal)
[Contrast Sets for Stativity of English Verbs in Context](https://aclanthology.org/2022.coling-1.354/) (Chen & Palmer, COLING 2022)
ACL
- Daniel Chen and Alexis Palmer. 2022. Contrast Sets for Stativity of English Verbs in Context. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4028–4036, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.