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. While no preference appears in production, we show that the proposed factors contribute to a small but consistent learning advantage of DLM for listeners of verb-initial languages.

Authors:

Yuqing Zhang: University of Groningen; Tessa Verhoef: Leiden University; Gertjan van Noord: University of Groningen; Arianna Bisazza: University of Groningen