Relation Extraction with Explanation
This work addresses the need for more interpretable relation extraction models in natural language processing, though it is incremental as it builds on existing neural models.
The paper tackled the problem of improving both accuracy and explainability in relation extraction models by using fine-grained entity types and generating distractor sentences for training. The methods achieved new state-of-the-art accuracy on the FB-NYT dataset while enhancing model explainability.
Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused on improving extraction accuracy but little is known about their explainability. In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. We demonstrate that replacing the entity mentions in the sentences with their fine-grained entity types not only enhances extraction accuracy but also improves explanation. We also propose to automatically generate "distractor" sentences to augment the bags and train the model to ignore the distractors. Evaluations on the widely used FB-NYT dataset show that our methods achieve new state-of-the-art accuracy while improving model explainability.