Papers by Tessa Verhoef
Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off (2023.tacl-1)
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| Challenge: | Existing models of language learning with neural agents lack appropriate cognitive biases in artificial learners. |
| Approach: | They propose a framework where speaking and listening agents learn a miniature language via supervised learning and optimize it for communication via reinforcement learning. |
| Outcome: | The proposed framework replicates the word-order/case-marking trade-off without hard-coding biases in the agents. |
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use (2025.findings-emnlp)
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| Challenge: | Lexical semantic change has been investigated with observational and experimental methods, but observational methods cannot get at causal mechanisms. |
| Approach: | They introduce a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system and then manipulating their communicative needs. |
| Outcome: | The proposed framework simulates the evolution of a lexical system within a single generation by grounding agents in a real lexicon and manipulating their communicative needs. |
Searching for Structure: Investigating Emergent Communication with Large Language Models (2025.coling-main)
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| Challenge: | Human languages have evolved to be structured through repeated language learning and use. |
| Approach: | They propose to use large language models to optimise for implicit biases that shape languages to improve communicative efficiency. |
| Outcome: | The proposed models can be used to study language evolution and open possibilities for human-machine interactions. |
The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning (2021.emnlp-main)
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| Challenge: | Existing studies have focused on agent-based simulations of language emergence. |
| Approach: | They propose to model the trade-off between word order and inflection in natural languages by using neural network agents. |
| Outcome: | The results show that neural agents strive to maintain the utterance type distribution observed during learning, rather than developing a more efficient or systematic language. |
Endowing Neural Language Learners with Human-like Biases: A Case Study on Dependency Length Minimization (2024.lrec-main)
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| Challenge: | Comparing the behavior of models with that of human learners can reveal which aspects affect the emergence of this preference. |
| Approach: | They propose to add three factors to the standard neural-agent language learning and communication framework to make the simulation more realistic. |
| Outcome: | The proposed conditions can contribute to a small but significant learning advantage for listeners of verb-initial languages. |