The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers

Zhang, Dongxiang; Wang, Lei; Zhang, Luming; Dai, Bing Tian; Shen, Heng Tao

doi:10.48550/arxiv.1808.07290

Cited by 1 publication

(1 citation statement)

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“…NLP in automated word problem solvers: Most of the recent works in automated word problem solving focus on solving simple math word problems involving single linear equations [14], unlike our domain. Many existing kinematics word problem solvers [9,5] completely bypass the natural language understanding step by taking the structured specification of the word problem as input.…”

Section: Related Workmentioning

confidence: 99%

Span Detection for Kinematics Word Problems

Abraham

Deepak

Sundaram

2023

Communications in Computer and Information Science

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Solving kinematics word problems is a specialized task which is best addressed through bespoke logical reasoners. Reasoners, however, require structured input in the form of kinematics parameter values, and translating textual word problems to such structured inputs is a key step in enabling end-to-end automated word problem solving. Span detection for a kinematics parameter is the process of identifying the smallest span of text from a kinematics word problem that has the information to estimate the value of that parameter. A key aspect differentiating kinematics span detection from other span detection tasks is the presence of multiple inter-related parameters for which separate spans need to be identified. State-of-the-art span detection methods are not capable of leveraging the existence of a plurality of inter-dependent span identification tasks. We propose a novel neural architecture that is designed to exploit the inter-relatedness between the separate span detection tasks using a single joint model. This allows us to train the same network for span detection over multiple kinematics parameters, implicitly and automatically transferring knowledge across the kinematics parameters. We show that such a joint training delivers an improvement of accuracies over real-world datasets against state-of-the-art methods for span detection.

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Section: Related Workmentioning

confidence: 99%