Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems

Abstract

Automatic math word problem solving has attracted growing attention in recent years. The evaluation datasets used by previous works have serious limitations in terms of scale and diversity. In this paper, we release a new large-scale and template-rich math word problem dataset named Ape210K. It consists of 210K Chinese elementary school-level math problems, which is 9 times the size of the largest public dataset Math23K. Each problem contains both the gold answer and the equations needed to derive the answer. Ape210K is also of greater diversity with 56K templates, which is 25 times more than Math23K. Our analysis shows that solving Ape210K requires not only natural language understanding but also commonsense knowledge. We expect Ape210K to be a benchmark for math word problem solving systems. Experiments indicate that state-of-the-art models on the Math23K dataset perform poorly on Ape210K. We propose a copy-augmented and feature-enriched sequence to sequence (seq2seq) model, which outperforms existing models by 3.2% on the Math23K dataset and serves as a strong baseline of the Ape210K dataset. The gap is still significant between human and our baseline model, calling for further research efforts. We make Ape210K dataset publicly available at https://github.com/yuantiku/ape210k

arXiv commentWe decide to withdraw this paper, since the proposed Ape210K dataset is not going public, the experiments in this paper is meaningless and irreproducible without access to the dataset. Please contact [email protected] if you have any questions

References in corpus (5)

• Distributed Representations of Words and Phrases and their Compositionality
• Language to Logical Form with Neural Attention
• Get To The Point: Summarization with Pointer-Generator Networks
• Data Recombination for Neural Semantic Parsing
• Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

Cited by in corpus (7)

• Training Verifiers to Solve Math Word Problems
• Unbiased Math Word Problems Benchmark for Mitigating Solving Bias
• Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning
• Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems
• Dynamic Framework for Collaborative Learning: Leveraging Advanced LLM with Adaptive Feedback Mechanisms
• Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
• Recall and Learn: A Memory-augmented Solver for Math Word Problems