Abstract
In embodied cognition, physical experiences are believed to shape abstract cognition, such as natural language and reasoning. Image schemas were introduced as spatio-temporal cognitive building blocks that capture these recurring sensorimotor experiences. The few existing approaches for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. Furthermore, the lack of sufficiently large, annotated datasets makes evaluation and supervised learning difficult. We propose to build on the recent success of large multilingual pretrained language models and a small dataset of examples from image schema literature to train a supervised classifier that classifies natural language expressions of varying lengths into image schemas. Despite most of the training data being in English with few examples for German, the model performs best in German. Additionally, we analyse the model’s zero-shot performance in Russian, French, and Mandarin. To further investigate the model’s behaviour, we utilize local linear approximations for prediction probabilities that indicate which words in a sentence the model relies on for its final classification decision. Code and dataset are publicly available.- Anthology ID:
- 2022.coling-1.493
- 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:
- 5571–5581
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.coling-1.493/
- DOI:
- Cite (ACL):
- Lennart Wachowiak and Dagmar Gromann. 2022. Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5571–5581, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing (Wachowiak & Gromann, COLING 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.coling-1.493.pdf