Learning grammar with a divide-and-concur neural network

• URL: http://arxiv.org/abs/2201.07341v1
• Date: Tue, 18 Jan 2022 22:42:43 GMT
• Title: Learning grammar with a divide-and-concur neural network
• Authors: Sean Deyo and Veit Elser
• Abstract summary: We implement a divide-and-concur iterative projection approach to context-free grammar inference. Our method requires a relatively small number of discrete parameters, making the inferred grammar directly interpretable.
• Score: 4.111899441919164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We implement a divide-and-concur iterative projection approach to context-free grammar inference. Unlike most state-of-the-art models of natural language processing, our method requires a relatively small number of discrete parameters, making the inferred grammar directly interpretable -- one can read off from a solution how to construct grammatically valid sentences. Another advantage of our approach is the ability to infer meaningful grammatical rules from just a few sentences, compared to the hundreds of gigabytes of training data many other models employ. We demonstrate several ways of applying our approach: classifying words and inferring a grammar from scratch, taking an existing grammar and refining its categories and rules, and taking an existing grammar and expanding its lexicon as it encounters new words in new data.

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