Papers with sentence processing
How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context (2020.tacl-1)
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| Challenge: | a recent study shows that context affects our perception of sentence acceptability, but few studies investigate how it affects language models. |
| Approach: | They compare acceptability ratings of sentences judged in isolation with a relevant context and with an irrelevant context. |
| Outcome: | The proposed model achieves state-of-the-art for unsupervised acceptability prediction. |
Surprisal Estimators for Human Reading Times Need Character Models (2021.acl-long)
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| Challenge: | Experimental results show that character models can be applied to a structural parser-based processing model to calculate word generation probabilities. |
| Approach: | They propose to use a character model to calculate word generation probabilities from a structural parser-based processing model. |
| Outcome: | The proposed model performs better on self-paced reading, eye-tracking, and fMRI data than large-scale language models trained on much more data. |
Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension (2021.findings-acl)
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| Challenge: | a prior work using surprisal only considered within-sentence context, using n-grams, neural language models, or syntactic structure as conditioning context. |
| Approach: | They extend the surprisal approach to use broader topical context . they identify distinct patterns of neural activation for lexical surprised and topical surpresed . |
| Outcome: | The proposed method captures effects of local and topical contexts on processing . it shows that local and broad contextual cues recruit different brain regions . |
A large-scale study of the effects of word frequency and predictability in naturalistic reading (N19-1)
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| Challenge: | Recent studies have shown separable effects of word frequency and predictability on human sentence processing . other theories hold that apparent effects of frequency are underlyingly effects of predictability . |
| Approach: | They examine the generalizability of this finding to more realistic conditions of sentence processing by studying effects of frequency and predictability in three large-scale naturalistic reading corpora. |
| Outcome: | The results show that word frequency and predictability are significant in isolation but not over and above predictability, and raise doubts about the existence of such a distinction in everyday sentence comprehension. |
Dual Alignment Between Language Model Layers and Human Sentence Processing (2026.acl-long)
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| Challenge: | Existing studies have demonstrated both the successes and limitations of accurate predictability estimation by modern LMs in cognitive modeling. |
| Approach: | They propose to use internal layers to better estimate human cognitive effort observed in syntactic ambiguity processing in English. |
| Outcome: | The proposed models can be modeled using surprisal from early layers of large language models (LLMs) this raises the question whether such advantages extend to more syntactically challenging constructions, where surprised estimates underestimate human cognitive effort. |