Abstract
Fluent speakers make implicit predictions about forthcoming linguistic items while processing sentences, possibly to increase efficiency in real-time comprehension. However, the extent to which prediction is the primary mode of processing human language is widely debated. The human language processor may also gain efficiency by integrating new linguistic information with prior knowledge and the preceding context, without actively predicting. At present, the role of probabilistic integration, as well as its computational foundation, remains relatively understudied. Here, we explored whether a Delayed Recurrent Neural Network (d-RNN, Turek et al., 2020), as an implementation of both prediction and integration, can explain patterns of human language processing over and above the contribution of a purely predictive RNN model. We found that incorporating integration contributes to explaining variability in eye-tracking data for English and Hindi.- Anthology ID:
- 2024.cmcl-1.9
- Volume:
- Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Tatsuki Kuribayashi, Giulia Rambelli, Ece Takmaz, Philipp Wicke, Yohei Oseki
- Venues:
- CMCL | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 101–108
- Language:
- URL:
- https://aclanthology.org/2024.cmcl-1.9
- DOI:
- Cite (ACL):
- Nina Delcaro, Luca Onnis, and Raquel Alhama. 2024. Predict but Also Integrate: an Analysis of Sentence Processing Models for English and Hindi. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 101–108, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Predict but Also Integrate: an Analysis of Sentence Processing Models for English and Hindi (Delcaro et al., CMCL-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.cmcl-1.9.pdf