Improving Unsupervised Constituency Parsing via Maximizing Semantic Information

• URL: http://arxiv.org/abs/2410.02558v3
• Date: Fri, 04 Apr 2025 11:11:58 GMT
• Title: Improving Unsupervised Constituency Parsing via Maximizing Semantic Information
• Authors: Junjie Chen, Xiangheng He, Yusuke Miyao, Danushka Bollegala,
• Abstract summary: Unsupervised constituencys organize phrases within a sentence into a tree-shaped syntactic constituent structure.<n>Traditional objective of maximizing sentence log-likelihood does not explicitly account for the close relationship between the constituent structure and the semantics.<n>We introduce a novel objective that trains parsings by maximizing SemInfo, the semantic information encoded in constituent structures.
• Score: 35.63321102040579
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Unsupervised constituency parsers organize phrases within a sentence into a tree-shaped syntactic constituent structure that reflects the organization of sentence semantics. However, the traditional objective of maximizing sentence log-likelihood (LL) does not explicitly account for the close relationship between the constituent structure and the semantics, resulting in a weak correlation between LL values and parsing accuracy. In this paper, we introduce a novel objective that trains parsers by maximizing SemInfo, the semantic information encoded in constituent structures. We introduce a bag-of-substrings model to represent the semantics and estimate the SemInfo value using the probability-weighted information metric. We apply the SemInfo maximization objective to training Probabilistic Context-Free Grammar (PCFG) parsers and develop a Tree Conditional Random Field (TreeCRF)-based model to facilitate the training. Experiments show that SemInfo correlates more strongly with parsing accuracy than LL, establishing SemInfo as a better unsupervised parsing objective. As a result, our algorithm significantly improves parsing accuracy by an average of 7.85 sentence-F1 scores across five PCFG variants and in four languages, achieving state-of-the-art level results in three of the four languages.

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