Abstract
We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.- Anthology ID:
- W19-2910
- Volume:
- Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Emmanuele Chersoni, Cassandra Jacobs, Alessandro Lenci, Tal Linzen, Laurent Prévot, Enrico Santus
- Venue:
- CMCL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 86–92
- Language:
- URL:
- https://aclanthology.org/W19-2910
- DOI:
- 10.18653/v1/W19-2910
- Cite (ACL):
- Shaorong Yan and Aaron Steven White. 2019. A Framework for Decoding Event-Related Potentials from Text. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 86–92, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- A Framework for Decoding Event-Related Potentials from Text (Yan & White, CMCL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W19-2910.pdf