1 citations · 1 across the 13 of their papers we have counts for
13 papers
Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations
Yuqing Zhang, Tessa Verhoef, Gertjan van Noord +1
Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principle known as dependency length min…
CAIT: A Syntactic Parsing Toolkit for Child-Adult InTeractions
Francesca Padovani, Xiulin Yang, Bastian Bunzeck +4
CHILDES is a paramount resource for language acquisition studies -- yet computational tools for analyzing its syntactic structure remain limited. Leveraging the recent release of t…
Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition
Francesca Padovani, Jaap Jumelet, Yevgen Matusevych +1
Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate this question using neural l…
Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models
Vitalii Hirak, Jaap Jumelet, Arianna Bisazza
Despite major advances in multilingual modeling, large quality disparities persist across languages. Besides the obvious impact of uneven training resources, typological properties…
A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Models
Xiulin Yang, Arianna Bisazza, Nathan Schneider +1
Several recent contributions have evaluated the Poverty of the Stimulus Hypothesis (PoSH) using Artificial Neural Networks (ANNs). The results suggest that ANN-based language model…
Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)
Francesca Padovani, Bastian Bunzeck, Manar Ali +4
We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we empl…
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
Jaap Jumelet, Abdellah Fourtassi, Akari Haga +23
We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally pla…
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang, Ecesu Ürker, Tessa Verhoef +2
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeli…
TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs
Ezgi Başar, Francesca Padovani, Jaap Jumelet +1
We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs).…
MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs
Jaap Jumelet, Leonie Weissweiler, Joakim Nivre +1
We introduce MultiBLiMP 1.0, a massively multilingual benchmark of linguistic minimal pairs, covering 101 languages and 2 types of subject-verb agreement, containing more than 128,…
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents
Yuchen Lian, Arianna Bisazza, Tessa Verhoef
Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM…
BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency
Akari Haga, Akiyo Fukatsu, Miyu Oba +2
While current large language models have achieved a remarkable success, their data efficiency remains a challenge to overcome. Recently it has been suggested that child-directed sp…
NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication
Yuchen Lian, Tessa Verhoef, Arianna Bisazza
Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols.…