Paper Overview
Field: NLP Authors: Ramon Ferrer-i-Cancho, Catherine Hobaiter, Thore Bergman Released: 2025-07-09 arXiv: 2507.06818
Summary
Dependency parsing consists of finding a tree representation for a sequence. Unsupervised dependency parsing aims to develop parsing methods without a gold standard during model training. In human languages, an unsupervised parser can be evaluated because some gold standard is usually available or can be created. For other species, a gold standard is unknown. Thus one may conclude that it is impossible to determine the accuracy of an unsupervised parser and, consequently, dependency parsing is unfeasible in other species.
However, the authors apply recent advances in network science to demonstrate that the proportion of correct edges retrieved by a parser must be high for the sequences of vocalizations or gestures that non-human primates produce, due to the fast decay of the sequence length distribution. In contrast, human language sequences lack this property. Therefore, evaluation without a gold standard is feasible in non-human primates, while it remains a hard problem in humans.
Key Implications
- Sequence length distributions with fast decay guarantee high parser accuracy without any gold standard.
- Non-human primate communication sequences (vocalizations, gestures) satisfy this property, making unsupervised parsing evaluation feasible.
- Human language sequences lack this property, so gold-standard-free evaluation remains difficult for human languages.