Oishik Chatterjee


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WARM: A Weakly (+Semi) Supervised Math Word Problem Solver
Oishik Chatterjee | Isha Pandey | Aashish Waikar | Vishwajeet Kumar | Ganesh Ramakrishnan
Proceedings of the 29th International Conference on Computational Linguistics

Solving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solving MWPs require full supervision in the form of intermediate equations. However, labeling every MWP with its corresponding equations is a time-consuming and expensive task. In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision. We approach this problem by first learning to generate the equation using the problem description and the final answer, which we subsequently use to train a supervised MWP solver. We propose and compare various weakly supervised techniques to learn to generate equations directly from the problem description and answer. Through extensive experiments, we demonstrate that without using equations for supervision, our approach achieves accuracy gains of 4.5% and 32% over the current state-of-the-art weakly-supervised approach, on the standard Math23K and AllArith datasets respectively. Additionally, we curate and release new datasets of roughly 10k MWPs each in English and in Hindi (a low-resource language). These datasets are suitable for training weakly supervised models. We also present an extension of our model to semi-supervised learning and present further improvements on results, along with insights.


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Semi-Supervised Data Programming with Subset Selection
Ayush Maheshwari | Oishik Chatterjee | Krishnateja Killamsetty | Ganesh Ramakrishnan | Rishabh Iyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021