@inproceedings{zhang-etal-2024-endowing,
    title = "Endowing Neural Language Learners with Human-like Biases: A Case Study on Dependency Length Minimization",
    author = "Zhang, Yuqing  and
      Verhoef, Tessa  and
      van Noord, Gertjan  and
      Bisazza, Arianna",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.516",
    pages = "5819--5832",
    abstract = "Natural languages show a tendency to minimize the linear distance between heads and their dependents in a sentence, known as dependency length minimization (DLM). Such a preference, however, has not been consistently replicated with neural agent simulations. Comparing the behavior of models with that of human learners can reveal which aspects affect the emergence of this phenomenon. In this work, we investigate the minimal conditions that may lead neural learners to develop a DLM preference. We add three factors to the standard neural-agent language learning and communication framework to make the simulation more realistic, namely: (i) the presence of noise during listening, (ii) context-sensitivity of word use through non-uniform conditional word distributions, and (iii) incremental sentence processing, or the extent to which an utterance{'}s meaning can be guessed before hearing it entirely. While no preference appears in production, we show that the proposed factors can contribute to a small but significant learning advantage of DLM for listeners of verb-initial languages.",
}

Natural languages show a tendency to minimize the linear distance between heads and their dependents in a sentence, known as dependency length minimization (DLM). Such a preference, however, has not been consistently replicated with neural agent simulations. Comparing the behavior of models with that of human learners can reveal which aspects affect the emergence of this phenomenon. In this work, we investigate the minimal conditions that may lead neural learners to develop a DLM preference. We add three factors to the standard neural-agent language learning and communication framework to make the simulation more realistic, namely: (i) the presence of noise during listening, (ii) context-sensitivity of word use through non-uniform conditional word distributions, and (iii) incremental sentence processing, or the extent to which an utterance{'}s meaning can be guessed before hearing it entirely. While no preference appears in production, we show that the proposed factors can contribute to a small but significant learning advantage of DLM for listeners of verb-initial languages.
Zhang, Yuqing Verhoef, Tessa van Noord, Gertjan Bisazza, Arianna
Endowing Neural Language Learners with Human-like Biases: A Case Study on Dependency Length Minimization