MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2109.00799v2)

Abstract: Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked soly on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple their models into highly reusable modules. We also provide a hyper-parameter search function to boost the performance. In total, we implement and compare 17 MWP solvers on 4 widely-used single equation generation benchmarks and 2 multiple equations generation benchmarks. These features enable our MWPToolkit to be suitable for researchers to reproduce advanced baseline models and develop new MWP solvers quickly. Code and documents are available at https://github.com/LYH-YF/MWPToolkit.

• The paper introduces MWPToolkit, the first open-source library that modularizes deep learning-based math word problem solvers across multiple benchmarks.
• It decouples core functions into config, data, model, and evaluation components to streamline hyper-parameter tuning and ensure reproducible research.
• It demonstrates robust performance with models like Graph2Tree for single equation tasks while highlighting avenues for future multi-equation research.

An Overview of MWPToolkit: A Comprehensive Framework for Math Word Problem Solvers

The paper presents a significant contribution to the field of NLP with its open-source framework MWPToolkit, dedicated to advancing the development of Math Word Problem (MWP) solvers. This toolkit addresses a notable void in the community by offering a unified, standardized platform conducive to both the reproduction of baseline models and the innovation of new solvers in the MWP domain. Below is an analysis that dissects the core components, features, and potential impacts of MWPToolkit.

Key Contributions and Structural Overview

MWPToolkit is articulated as the first open-source library specifically curated for deep learning-based MWP solvers. The initiative aims to streamline research processes by providing a modular and extensible framework that disaggregates MWP-solving methodologies into reusable components. Unlike preceding approaches, which often focus narrowly on one or two datasets, MWPToolkit encompasses a broad spectrum of 17 MWP solvers evaluated on six prominent benchmarks, thereby fostering a more equitable and exhaustive comparison landscape.

The architecture of MWPToolkit involves several distinct components: config, data, model, and evaluation. By decoupling these elements into modular units, the framework offers an efficient pathway for academia and industry to experiment with various models and datasets through straightforward configurations and execution protocols. The toolkit integrates a comprehensive hyper-parameter search functionality, promoting fine-tuning effectiveness while mitigating issues related to sub-optimal parameter settings.

Dataset Scope and Model Implementations

MWPToolkit incorporates six frequently utilized datasets encompassing both single and multiple equation generation tasks. The datasets, such as Math23K and HMWP, are processed through a unified schema, ensuring consistent experimentation protocols. Furthermore, the toolkit spans a diverse suite of models such as Seq2Seq, Seq2Tree, Graph2Tree, and pre-trained architectures like BERTGen and RoBERTaGen. These implementations ensure the framework captures a broad methodological spectrum, embedding traditional and state-of-the-art paradigms within one platform.

Performance Evaluation and Discourse

The paper meticulously evaluates the implemented models, setting criteria such as equation accuracy and answer accuracy, and provides an evaluative comparison across benchmark datasets. For instance, models like Graph2Tree demonstrated superior performance in single equation generation, thereby suggesting their robustness in tackling MWP tasks. Conversely, alternative models like GPT-2 showcased potential on multiple equations, indicating avenues for future research on multi-equation tasks.

Implications and Future Directions

MWPToolkit serves as a critical facilitator for advancing MWP solvers by not only standardizing evaluation metrics but by also enhancing reproducibility—a longstanding issue in the research domain. This framework could stimulate the development of more robust solvers by enabling rapid prototyping and testing in a uniform environment. Furthermore, the open-source nature ensures community contributions that could expand its capabilities and integrate novel algorithms efficiently.

As the toolkit evolves, future work could involve integrating additional datasets and incorporating new advancements such as neural-symbolic approaches or leveraging multilingual MWP solvers, as indicated by contemporary research trends. The paper highlights the integrative potential of MWPToolkit for fostering innovative approaches that could more effectively leverage the complexities intrinsic to MWPs.

Conclusion

MWPToolkit stands as a pivotal resource in the math word problem-solving community, harmonizing various models and datasets under a unified platform. It opens pathways for researchers to conduct systematic evaluations and innovate beyond current benchmarks effectively. The cogent establishment of this framework emphasizes its utility and value in driving forward NLP research centered on mathematical comprehension and problem-solving.