Papers with grammar induction systems

2 papers
Categorial grammar induction from raw data (2023.findings-acl)

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Challenge: a new model for categorial grammar induction is based on raw data without part-of-speech information.
Approach: They propose a grammar induction model that learns from raw data without part-of-speech information.
Outcome: a new model for inducing a basic categorial grammar is developed . the model attains a recall-homogeneity of 0.33 on average, and a bias toward forward function application is added .
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction (D18-1)

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Challenge: Recent attempts to improve grammar induction systems by bounding complexity of the model have not been compared against unbounded induction models.
Approach: They propose to use a Bayesian induction inducer to limit the search space of the model and then sample trees with or without bounding.
Outcome: The proposed model produces trees more accurately than or competitively with state-of-the-art constituency grammar induction models.

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