Relation extraction is a key task in Natural Language Processing (NLP), which
aims to extract relations between entity pairs from given texts. Recently,
relation extraction (RE) has achieved remarkable progress with the development
of deep neural networks. Most existing research focuses on constructing
explicit structured features using external knowledge such as knowledge graph
and dependency tree. In this paper, we propose a novel method to extract
multi-granularity features based solely on the original input sentences. We
show that effective structured features can be attained even without external
knowledge. Three kinds of features based on the input sentences are fully
exploited, which are in entity mention level, segment level, and sentence
level. All the three are jointly and hierarchically modeled. We evaluate our
method on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred
Revisited. To verify the effectiveness, we apply our method to different
encoders such as LSTM and BERT. Experimental results show that our method
significantly outperforms existing state-of-the-art models that even use
external knowledge. Extensive analyses demonstrate that the performance of our
model is contributed by the capture of multi-granularity features and the model
of their hierarchical structure. Code and data are available at
https://github.com/xnliang98/sms.

本篇论文提出了一种基于原始输入句子提取多粒度特征的新方法，该方法包含实体提及级别、片段级别和句子级别三种特征，并采用层次化建模方法，以解决自然语言处理中的关系提取问题，实验证明该模型性能优于甚至使用外部知识的现有最先进模型。

建模多粒度分层特征以进行关系提取

Modeling Multi-Granularity Hierarchical Features for Relation Extraction

One of the leading single-channel speech separation (SS) models is based on a
TasNet with a dual-path segmentation technique, where the size of each segment
remains unchanged throughout all layers. In contrast, our key finding is that
multi-granularity features are essential for enhancing contextual modeling and
computational efficiency. We introduce a self-attentive network with a novel
sandglass-shape, namely Sandglasset, which advances the state-of-the-art (SOTA)
SS performance at significantly smaller model size and computational cost.
Forward along each block inside Sandglasset, the temporal granularity of the
features gradually becomes coarser until reaching half of the network blocks,
and then successively turns finer towards the raw signal level. We also unfold
that residual connections between features with the same granularity are
critical for preserving information after passing through the bottleneck layer.
Experiments show our Sandglasset with only 2.3M parameters has achieved the
best results on two benchmark SS datasets -- WSJ0-2mix and WSJ0-3mix, where the
SI-SNRi scores have been improved by absolute 0.8 dB and 2.4 dB, respectively,
comparing to the prior SOTA results.

我们提出了一种名为 Sandglasset 的自注意力网络，它采用多粒度特征，成功地提高了语音分离性能，与先前最先进结果相比，模型更小，计算成本更低，且在两个基准语音分离数据集上都表现出最佳结果。