Paper-Conference

CAIT: A Syntactic Parsing Toolkit for Child–Adult InTeractions
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Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition
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Modeling Human-Like Color Naming Behavior in Context
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A Morphology-Aware Evaluation of Turkish Syntax in Large Language Models
Minimal pair benchmarks have become a common approach for evaluating the syntactic knowledge of language models (LMs). However, the …
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Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models
Despite major advances in multilingual modeling, large quality disparities persist across languages. Besides the obvious impact of …
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BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a …
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Steering Large Language Models for Machine Translation Personalization
Large language models have simplified the production of personalized translations reflecting predefined stylistic constraints. However, …
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Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement
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Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can outperform …
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Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)
We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based …
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