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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events. This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models. Our method leverages logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model. Improving the global consistency of predictions, our approachdoi:10.18653/v1/2020.acl-main.499 fatcat:6zdjzhxuk5e6hlpnpzdhy63u74