The noisy labeling problem has been one of the major obstacles for distant
supervised relation extraction. Existing approaches usually consider that the
noisy sentences are useless and will harm the model's performance. Therefore,
they mainly alleviate this problem by reducing the influence of noisy
sentences, such as applying bag-level selective attention or removing noisy
sentences from sentence-bags. However, the underlying cause of the noisy
labeling problem is not the lack of useful information, but the missing
relation labels. Intuitively, if we can allocate credible labels for noisy
sentences, they will be transformed into useful training data and benefit the
model's performance. Thus, in this paper, we propose a novel method for distant
supervised relation extraction, which employs unsupervised deep clustering to
generate reliable labels for noisy sentences. Specifically, our model contains
three modules: a sentence encoder, a noise detector and a label generator. The
sentence encoder is used to obtain feature representations. The noise detector
detects noisy sentences from sentence-bags, and the label generator produces
high-confidence relation labels for noisy sentences. Extensive experimental
results demonstrate that our model outperforms the state-of-the-art baselines
on a popular benchmark dataset, and can indeed alleviate the noisy labeling
problem.

本文提出了一种利用无监督深度聚类技术解决噪声标注问题的远程监督关系抽取模型，包括句子编码器、噪声检测器和标签生成器，实验证明该模型在常用基准数据集上优于现有的同类模型，并能够应对噪声标注问题。

嘈杂的句子是否对远程监督关系抽取毫无用处？

Are Noisy Sentences Useless for Distant Supervised Relation Extraction?

Distant supervised relation extraction has been successfully applied to large
corpus with thousands of relations. However, the inevitable wrong labeling
problem by distant supervision will hurt the performance of relation
extraction. In this paper, we propose a method with neural noise converter to
alleviate the impact of noisy data, and a conditional optimal selector to make
proper prediction. Our noise converter learns the structured transition matrix
on logit level and captures the property of distant supervised relation
extraction dataset. The conditional optimal selector on the other hand helps to
make proper prediction decision of an entity pair even if the group of
sentences is overwhelmed by no-relation sentences. We conduct experiments on a
widely used dataset and the results show significant improvement over
competitive baseline methods.

本文提出了一种基于神经噪声转换器方法和条件最优选择器的远程监督关系抽取方法，以减轻噪声数据造成的影响并帮助进行准确预测，实验证明该方法在大数据集中的效果显著提高。

使用神经噪声转换器和条件最优选择器改进远程监督关系抽取

Improving Distantly Supervised Relation Extraction with Neural Noise  Converter and Conditional Optimal Selector

Distant supervised relation extraction is an efficient approach to scale
relation extraction to very large corpora, and has been widely used to find
novel relational facts from plain text. Recent studies on neural relation
extraction have shown great progress on this task via modeling the sentences in
low-dimensional spaces, but seldom considered syntax information to model the
entities. In this paper, we propose to learn syntax-aware entity embedding for
neural relation extraction. First, we encode the context of entities on a
dependency tree as sentence-level entity embedding based on tree-GRU. Then, we
utilize both intra-sentence and inter-sentence attentions to obtain sentence
set-level entity embedding over all sentences containing the focus entity pair.
Finally, we combine both sentence embedding and entity embedding for relation
classification. We conduct experiments on a widely used real-world dataset and
the experimental results show that our model can make full use of all
informative instances and achieve state-of-the-art performance of relation
extraction.

本文提出了一种基于语法树和句子嵌入的远程监督关系抽取方法，使用句子和实体的嵌入对关系进行分类，实验结果表明这种方法在真实数据集上取得了最佳的性能。