Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
This addresses noise in relation extraction for NLP applications, but it is incremental as it builds on prior attention-based methods.
The paper tackles noisy training data in distant supervision relation extraction by proposing a neural method that uses intra-bag and inter-bag attentions to reduce noise at sentence and bag levels, achieving better accuracy than state-of-the-art methods on the New York Times dataset.
This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.