  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.
