Article

Neural Language Models Capture Some, But Not All, Agreement Attraction Effects

2020-02-12

Abstract excerpt

<p>The number of the subject in English must match the number of the corresponding verb (dog runs but dogs run). Yet in real-time language production and comprehension, speakers often mistakenly compute agreement between the verb and a grammatically irrelevant non-subject noun phrase instead. This phenomenon, referred to as agreement attraction, is modulated by a wide range of factors; any complete computational m...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3f6a19dd-1a16-5003-8fd8-e1b4fbf0f87f
DOI: 10.31234/osf.io/97qcg

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Neural Language Models Capture Some, But Not All, Agreement Attraction EffectsDOI 10.31234/osf.io/97qcgRead

BLM-Hum1800: A Human-Validated Benchmark for Linguistic Competence in English and FrenchSemantic proximity Read

The Road Not Taken: Unsaid Word Alternatives are Represented in the BrainSemantic proximity Read

Neural Language Models’ sensitivity to long-distance dependencies and their island constraints is not universal: a comparison between English and Dutch humans and modelsSemantic proximity Read

Overlap in Meaning Is a Stronger Predictor of Semantic Activation in GPT-3 Than in HumansSemantic proximity Read

Emergent Numeric Bias in Large Language Models: An Empirical Study on the Anomalous Recurrence of the Number 27 Across Independent SessionsSemantic proximity Read

Core vocabulary reveals differences between human word prediction and large language modelsSemantic proximity Read

SuperGLUE: The AI raceSemantic proximity Read

Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche’Semantic proximity Read

Core vocabulary reveals differences between human word prediction and large language modelsSemantic proximity Read

Modeling word and morpheme order in natural language as an efficient tradeoff of memory and surprisalSemantic proximity Read

Can Large Language Models Help Augment English Psycholinguistic Datasets?Semantic proximity Read

The roles of neural networks in language acquisitionSemantic proximity Read