TY - JOUR AU - Sundaram, Sowmya S AU - P, Deepak AU - Abraham, Savitha Sam PY - 2020/04/03 Y2 - 2024/03/28 TI - Distributed Representations for Arithmetic Word Problems JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 05 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v34i05.6432 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6432 SP - 9000-9007 AB - <p>We consider the task of learning distributed representations for arithmetic word problems. We outline the characteristics of the domain of arithmetic word problems that make generic text embedding methods inadequate, necessitating a specialized representation learning method to facilitate the task of retrieval across a wide range of use cases within online learning platforms. Our contribution is two-fold; first, we propose several 'operators' that distil knowledge of the domain of arithmetic word problems and schemas into word problem transformations. Second, we propose a novel neural architecture that combines LSTMs with graph convolutional networks to leverage word problems and their operator-transformed versions to learn distributed representations for word problems. While our target is to ensure that the distributed representations are schema-aligned, we do not make use of schema labels in the learning process, thus yielding an unsupervised representation learning method. Through an evaluation on retrieval over a publicly available corpus of word problems, we illustrate that our framework is able to consistently improve upon contemporary generic text embeddings in terms of schema-alignment.</p> ER -