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Found 16 papers, 7 papers with code
CMCL 2022 Shared Task on Multilingual and Crosslingual Prediction of Human Reading Behavior
We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL).
CMCL 2021 Shared Task on Eye-Tracking Prediction
The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo).
Tree-Planted Transformers: Large Language Models with Implicit Syntactic Supervision
Large Language Models (LLMs) have achieved remarkable success thanks to scalability on large text corpora, but have some drawback in training efficiency.
Emergent Word Order Universals from Cognitively-Motivated Language Models
This also showcases the advantage of cognitively-motivated LMs, which are typically employed in cognitive modeling, in the computational simulation of language universals.
Psychometric Predictive Power of Large Language Models
In other words, pure next-word probability remains a strong predictor for human reading behavior, even in the age of LLMs.
JCoLA: Japanese Corpus of Linguistic Acceptability
In this paper, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which consists of 10, 020 sentences annotated with binary acceptability judgments.
Composition, Attention, or Both?
In this paper, we propose a novel architecture called Composition Attention Grammars (CAGs) that recursively compose subtrees into a single vector representation with a composition function, and selectively attend to previous structural information with a self-attention mechanism.
Context Limitations Make Neural Language Models More Human-Like
Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading.
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like.
Lower Perplexity is Not Always Human-Like
Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
Effective Batching for Recurrent Neural Network Grammars
However, RNNGs are known to be harder to scale due to the difficulty of batched training.
Design of BCCWJ-EEG: Balanced Corpus with Human Electroencephalography
Importantly, this inter-fertilization between NLP, on one hand, and the cognitive (neuro)science of language, on the other, has been driven by the language resources annotated with human language processing data.
Inverting and Modeling Morphological Inflection
Previous {``}wug{''} tests (Berko, 1958) on Japanese verbal inflection have demonstrated that Japanese speakers, both adults and children, cannot inflect novel present tense forms to {``}correct{''} past tense forms predicted by rules of existent verbs (de Chene, 1982; Vance, 1987, 1991; Klafehn, 2003, 2013), indicating that Japanese verbs are merely stored in the mental lexicon.
Modeling Hierarchical Syntactic Structures in Morphological Processing
Sentences are represented as hierarchical syntactic structures, which have been successfully modeled in sentence processing.
