HMS: A Hierarchical Solver with Dependency-Enhanced Understanding for Math Word Problem
نویسندگان
چکیده
Automatically solving math word problems is a crucial task for exploring the intelligence levels of machines in general AI domain. It highly challenging since it requires not only natural language understanding but also mathematical expression inference. Existing solutions usually explore sequence-to-sequence models to generate expressions, where are simply encoded sequentially. However, such generally far from enough as similar humans and lead incorrect answers. To this end, paper, we propose novel Hierarchical Math Solver (HMS) make deep exploitation problems. In problem understanding, imitating human reading habits, hierarchical word-clause-problem encoder. Specifically, first split each into several clauses learn semantics local clause level global level. Then, dependency-based module enhance with dependency structure problem. Next, inference, tree-based decoder answer. decoder, apply attention mechanism context different levels, pointer-generator network guide model copy existing information infer extra knowledge. Extensive experimental results on two widely used datasets demonstrate that HMS achieves better answers more reasonable
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2021
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v35i5.16547