Papers by Takateru Yamakoshi

4 papers
Evaluating distillation methods for data-efficient syntax learning (2025.findings-emnlp)

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Challenge: knowledge distillation (KD) targeting attention should selectively accelerate syntax acquisition, a study finds . logit-based KD dramatically improves data-efficiency, attention-based one provides minimal benefit even for syntactic tasks.
Approach: a study predicts that knowledge distillation targeting attention should selectively accelerate syntax acquisition . a systolic analysis of student models compared to logit-based knowledge distillations .
Outcome: a new study shows that knowledge distillation (KD) targeting attention accelerates syntax acquisition . the hypothesis is tested on syntactic benchmarks and perplexity.
Probing BERT’s priors with serial reproduction chains (2022.findings-acl)

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Challenge: Large neural language models have induced surprisingly human-like linguistic knowledge, from syntactic structure and subtle lexical biases to more insidious social biase and stereotypes.
Approach: They propose to use serial reproduction chains to generate representative samples from popular masked language models like BERT to test their hypothesis.
Outcome: The proposed method is based on theories of iterated learning in cognitive science and can be used to probe masked language models.
Investigating representations of verb bias in neural language models (2020.emnlp-main)

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Challenge: Languages typically provide more than one grammatical construction to express certain types of messages.
Approach: They propose a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation.
Outcome: The proposed model outperforms recurrent architectures even under comparable parameter and training settings.
Causal interventions expose implicit situation models for commonsense language understanding (2023.findings-acl)

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Challenge: Classical psycholinguistic accounts have suggested that world knowledge enters into language understanding through structured schemas called situation models.
Approach: They apply causal intervention techniques to transformer models to analyze performance on the Winograd Schema Challenge .
Outcome: The proposed model performs well on the Winograd Schema Challenge .

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