Abstract
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.
Abstract (translated)
针对远程监控产生的噪声训练数据，提出了一种神经关系提取方法。以往的研究主要集中在句子级的去噪上，主要是通过设计神经网络来考虑句内噪声。为了分别处理句子级和包级的噪声，本文考虑了包内和包间的注意事项。首先，利用包内注意事项对句子嵌入进行加权，计算关系感知的包表示。在这里，每个可能的关系都被用作注意力计算的查询，而不是在传统方法中只使用目标关系。此外，利用基于相似性的包间注意模块，通过对包表示进行加权，计算出训练集中一组具有相同关系标签的包的表示。最后，在建立关系提取器时，使用一个包组作为训练样本。纽约时报数据集的实验结果证明了我们提出的包内和包间注意模块的有效性。我们的方法在这个数据集上也比最先进的方法获得了更好的关系提取精度。
URL
https://arxiv.org/abs/1904.00143
