Data-Driven Methods for Solving Algebra Word Problems
Benjamin Robaidek, Rik Koncel-Kedziorski, Hannaneh Hajishirzi

TL;DR
This paper evaluates data-driven neural methods for solving algebra word problems, demonstrating that neural classifiers outperform complex models on large datasets, but highlighting the need for semantic and world knowledge for further progress.
Contribution
It shows that well-tuned neural equation classifiers can outperform more complex models on algebra word problems, emphasizing the importance of semantic understanding.
Findings
Neural classifiers outperform sequence-to-sequence models on large datasets.
Error analysis reveals the need for semantic and world knowledge.
Fully data-driven models show promise but have limitations.
Abstract
We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets. We show that well-tuned neural equation classifiers can outperform more sophisticated models such as sequence to sequence and self-attention across these datasets. Our error analysis indicates that, while fully data driven models show some promise, semantic and world knowledge is necessary for further advances.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Machine Learning and Data Classification
