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      Text2Math: End-to-end Parsing Text into Math Expressions

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          Abstract

          We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.

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          Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

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            Deep Neural Solver for Math Word Problems

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              Solving General Arithmetic Word Problems

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                Author and article information

                Journal
                15 October 2019
                Article
                1910.06571
                cb680113-886d-4d60-a669-80cc31f54bcf

                http://creativecommons.org/licenses/by-nc-sa/4.0/

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                Custom metadata
                Accepted by EMNLP2019
                cs.CL

                Theoretical computer science
                Theoretical computer science

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