Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems

• URL: http://arxiv.org/abs/2010.06823v1
• Date: Wed, 14 Oct 2020 06:27:07 GMT
• Title: Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems
• Authors: Jinghui Qin, Lihui Lin, Xiaodan Liang, Rumin Zhang, Liang Lin
• Abstract summary: A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs. We propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of various MWPs uniformly. Then a semantically-aligned universal tree-structured solver (SAU-r) based on an encoder-decoder framework is proposed to resolve multiple types of MWPs in a unified model.
• Score: 129.90766822085132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs. Herein, we propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of various MWPs uniformly. Then a semantically-aligned universal tree-structured solver (SAU-Solver) based on an encoder-decoder framework is proposed to resolve multiple types of MWPs in a unified model, benefiting from our UET representation. Our SAU-Solver generates a universal expression tree explicitly by deciding which symbol to generate according to the generated symbols' semantic meanings like human solving MWPs. Besides, our SAU-Solver also includes a novel subtree-level semanticallyaligned regularization to further enforce the semantic constraints and rationality of the generated expression tree by aligning with the contextual information. Finally, to validate the universality of our solver and extend the research boundary of MWPs, we introduce a new challenging Hybrid Math Word Problems dataset (HMWP), consisting of three types of MWPs. Experimental results on several MWPs datasets show that our model can solve universal types of MWPs and outperforms several state-of-the-art models.

Related papers

• SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models [86.478087039015]
  We present a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. Based on our proposed joint mixing, we propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. We hope our work may cast a light on the exploration of joint mixing in future MLLM research.
  arXiv  Detail & Related papers  (2023-11-13T18:59:47Z)
• Solving Math Word Problem with Problem Type Classification [12.700472956406005]
  Math word problems (MWPs) require analyzing text descriptions and generating mathematical equations to derive solutions. Existing works focus on solving MWPs with two types of solvers: tree-based solver and large language model (LLM) solver. This paper utilizes multiple ensemble approaches to improve MWP-solving ability.
  arXiv  Detail & Related papers  (2023-08-26T10:35:16Z)
• Analogical Math Word Problems Solving with Enhanced Problem-Solution Association [37.70402758178867]
  We propose to build a novel MWP solver by leveraging analogical MWPs. The key idea, named analogy identification, is to associate the analogical MWP pairs in a latent space. A solution discriminator is integrated into the MWP solver to enhance the association between the representations of MWPs and their true solutions.
  arXiv  Detail & Related papers  (2022-12-01T19:50:30Z)
• Generalizing Math Word Problem Solvers via Solution Diversification [56.2690023011738]
  We design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator. Our framework is flexibly applicable to a wide setting of fully, semi-weakly and weakly supervised training for all Seq2Seq MWP solvers.
  arXiv  Detail & Related papers  (2022-12-01T19:34:58Z)
• LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning [135.8654475934613]
  We first construct a high-quality MWP dataset named InterMWP which consists of 11,495 MWPs. We propose a novel approach with logical prompt and interpretation, called Logicr. With these improved semantic representations, our Logicr generates corresponding solution expressions and interpretable knowledge in accord with the generated solution expressions.
  arXiv  Detail & Related papers  (2022-05-17T11:01:52Z)
• Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints [37.493809561634386]
  We study the problem of generating arithmetic math word problems (MWPs) given a math equation. Existing approaches are prone to generating MWPs that are mathematically invalid or have unsatisfactory language quality.
  arXiv  Detail & Related papers  (2021-09-09T20:24:25Z)
• Generate & Rank: A Multi-task Framework for Math Word Problems [48.99880318686938]
  Math word problem (MWP) is a challenging and critical task in natural language processing. We propose Generate & Rank, a framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions.
  arXiv  Detail & Related papers  (2021-09-07T12:21:49Z)
• MWP-BERT: A Strong Baseline for Math Word Problems [47.51572465676904]
  Math word problem (MWP) solving is the task of transforming a sequence of natural language problem descriptions to executable math equations. Although recent sequence modeling MWP solvers have gained credits on the math-text contextual understanding, pre-trained language models (PLM) have not been explored for solving MWP. We introduce MWP-BERT to obtain pre-trained token representations that capture the alignment between text description and mathematical logic.
  arXiv  Detail & Related papers  (2021-07-28T15:28:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.