A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers

• URL: http://arxiv.org/abs/2106.15772v1
• Date: Wed, 30 Jun 2021 01:54:11 GMT
• Title: A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers
• Authors: Shen-Yun Miao, Chao-Chun Liang, Keh-Yih Su
• Abstract summary: We present ASDiv, a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school.
• Score: 10.244215079409797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.

Related papers

• Cutting Through the Noise: Boosting LLM Performance on Math Word Problems [52.99006895757801]
  Large Language Models excel at solving math word problems, but struggle with real-world problems containing irrelevant information. We propose a prompting framework that generates adversarial variants of MWPs by adding irrelevant variables. Fine-tuning on adversarial training instances improves performance on adversarial MWPs by 8%.
  arXiv  Detail & Related papers  (2024-05-30T18:07:13Z)
• What Makes Math Word Problems Challenging for LLMs? [5.153388971862429]
  We conduct an in-depth analysis of the key linguistic and mathematical characteristics of math word problems (MWPs) We train feature-based classifiers to better understand the impact of each feature on the overall difficulty of MWPs for prominent large language models (LLMs)
  arXiv  Detail & Related papers  (2024-03-17T23:18:40Z)
• Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem [58.3723958800254]
  Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. They are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP)
  arXiv  Detail & Related papers  (2024-03-06T09:06:34Z)
• MWPRanker: An Expression Similarity Based Math Word Problem Retriever [12.638925774492403]
  Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences. We propose a tool in this work for MWP retrieval.
  arXiv  Detail & Related papers  (2023-07-03T15:44:18Z)
• 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)
• Adversarial Examples for Evaluating Math Word Problem Solvers [4.266990593059533]
  Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. The extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear. We generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers.
  arXiv  Detail & Related papers  (2021-09-13T12:47:40Z)
• 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)
• Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems [129.90766822085132]
  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.
  arXiv  Detail & Related papers  (2020-10-14T06:27:07Z)

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.