Papers by Andrea de Varda
Scaling in Cognitive Modelling: a Multilingual Approach to Human Reading Times (2023.acl-short)
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| Challenge: | Neural language models provide conditional probability distributions over the lexicon that are predictive of human processing times. |
| Approach: | They propose to use a transformer-based model to generate probabilistic estimates that are less predictive of early eye-tracking measurements reflecting lexical access and early semantic integration. |
| Outcome: | The proposed models show that larger models capture late eye-tracking measurements that reflect the full integration of a word into the current language context. |
The Effects of Surprisal across Languages: Results from Native and Non-native Reading (2022.findings-aacl)
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| Challenge: | Context-dependent predictive processes have been proposed as a core component of the human cognitive system. |
| Approach: | They extract surprisal estimates from mBERT and assess their predictive power on the MECO corpus, a cross-linguistic dataset of eye movement behavior in reading. |
| Outcome: | The proposed model is based on a cross-linguistic dataset of eye movement behavior in reading. |