Abstract
We study the problem of integrating cognitive language processing signals (e.g., eye-tracking or EEG data) into pre-trained language models like BERT. Existing methods typically fine-tune pre-trained models on cognitive data, ignoring the semantic gap between the texts and cognitive signals. To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks. Extensive experiments show that: (1) Cognition-guided pre-trained models can consistently perform better than basic pre-trained models on ten NLP tasks. (2) Different cognitive features contribute differently to different NLP tasks. Based on this observation, we give a fine-grained explanation of why cognitive data is helpful for NLP. (3) Different transformer layers of pre-trained models should encode different cognitive features, with word-level cognitive features at the bottom and semantic-level cognitive features at the top. (4) Attention visualization demonstrates that CogBERT aligns with human gaze patterns and improves its natural language comprehension ability.- Anthology ID:
- 2022.coling-1.284
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3210–3225
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.284
- DOI:
- Cite (ACL):
- Xiao Ding, Bowen Chen, Li Du, Bing Qin, and Ting Liu. 2022. CogBERT: Cognition-Guided Pre-trained Language Models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3210–3225, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- CogBERT: Cognition-Guided Pre-trained Language Models (Ding et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.coling-1.284.pdf
- Code
- PosoSAgapo/cogbert
- Data
- CoNLL-2003, GLUE, QNLI