Learning from Explanations with Neural Module Execution Tree

While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data. To make the most of each example, previous work has introduced natural language (NL) explanations to serve as supplements to mere labels. Such NL explanations can provide sufficient domain knowledge for generating more labeled data over new instances, while the annotation time only doubles. However, directly applying the NL explanations for augmenting model learning encounters two challenges. First, NL explanations are unstructured and inherently compositional, which asks for modularized model to represent their semantics. Second, NL explanations often have large numbers of linguistic variants, resulting in low recall and limited generalization ability when applied to unlabeled data. In this paper, we propose a novel Neural Modular Execution Tree (NMET) framework for augmenting sequence classification with NL explanations. After transforming NL explanations into executable logical forms with a semantic parser, NMET employs a neural module network architecture to generalize different type of actions (specified by the logical forms) for labeling data instances, and accumulates the results with soft logic, which substantially increases the coverage of each NL explanation. Experiments on two NLP tasks, relation extraction and sentiment analysis, demonstrate its superiority over baseline methods by leveraging NL explanation. Its extension to multi-hop question answering achieves performance gain with light annotation effort. Also, NMET achieves much better performance compared to traditional label-only supervised models in the same annotation time.