Negation, Coordination, and Quantifiers in Contextualized Language Models
Aikaterini-Lida Kalouli, Rita Sevastjanova, Christin Beck, Maribel Romero
Abstract
With the success of contextualized language models, much research explores what these models really learn and in which cases they still fail. Most of this work focuses on specific NLP tasks and on the learning outcome. Little research has attempted to decouple the models’ weaknesses from specific tasks and focus on the embeddings per se and their mode of learning. In this paper, we take up this research opportunity: based on theoretical linguistic insights, we explore whether the semantic constraints of function words are learned and how the surrounding context impacts their embeddings. We create suitable datasets, provide new insights into the inner workings of LMs vis-a-vis function words and implement an assisting visual web interface for qualitative analysis.- Anthology ID:
- 2022.coling-1.272
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3074–3085
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.272
- DOI:
- Bibkey:
- Cite (ACL):
- Aikaterini-Lida Kalouli, Rita Sevastjanova, Christin Beck, and Maribel Romero. 2022. Negation, Coordination, and Quantifiers in Contextualized Language Models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3074–3085, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Negation, Coordination, and Quantifiers in Contextualized Language Models (Kalouli et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.272.pdf
- Data
- LAMA
Export citation
@inproceedings{kalouli-etal-2022-negation,
title = "Negation, Coordination, and Quantifiers in Contextualized Language Models",
author = "Kalouli, Aikaterini-Lida and
Sevastjanova, Rita and
Beck, Christin and
Romero, Maribel",
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-1.272",
pages = "3074--3085",
abstract = "With the success of contextualized language models, much research explores what these models really learn and in which cases they still fail. Most of this work focuses on specific NLP tasks and on the learning outcome. Little research has attempted to decouple the models{'} weaknesses from specific tasks and focus on the embeddings per se and their mode of learning. In this paper, we take up this research opportunity: based on theoretical linguistic insights, we explore whether the semantic constraints of function words are learned and how the surrounding context impacts their embeddings. We create suitable datasets, provide new insights into the inner workings of LMs vis-a-vis function words and implement an assisting visual web interface for qualitative analysis.",
}
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%0 Conference Proceedings %T Negation, Coordination, and Quantifiers in Contextualized Language Models %A Kalouli, Aikaterini-Lida %A Sevastjanova, Rita %A Beck, Christin %A Romero, Maribel %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 kalouli-etal-2022-negation %X With the success of contextualized language models, much research explores what these models really learn and in which cases they still fail. Most of this work focuses on specific NLP tasks and on the learning outcome. Little research has attempted to decouple the models’ weaknesses from specific tasks and focus on the embeddings per se and their mode of learning. In this paper, we take up this research opportunity: based on theoretical linguistic insights, we explore whether the semantic constraints of function words are learned and how the surrounding context impacts their embeddings. We create suitable datasets, provide new insights into the inner workings of LMs vis-a-vis function words and implement an assisting visual web interface for qualitative analysis. %U https://aclanthology.org/2022.coling-1.272 %P 3074-3085
Markdown (Informal)
[Negation, Coordination, and Quantifiers in Contextualized Language Models](https://aclanthology.org/2022.coling-1.272) (Kalouli et al., COLING 2022)
- Negation, Coordination, and Quantifiers in Contextualized Language Models (Kalouli et al., COLING 2022)
ACL
- Aikaterini-Lida Kalouli, Rita Sevastjanova, Christin Beck, and Maribel Romero. 2022. Negation, Coordination, and Quantifiers in Contextualized Language Models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3074–3085, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.