We provide a study of using the biaffine model for neural discourse
dependency parsing and achieve significant performance improvement compared
with the baseline parsers. We compare the Eisner algorithm and the
Chu-Liu-Edmonds algorithm in the task and find that using the Chu-Liu-Edmonds
algorithm generates deeper trees and achieves better performance. We also
evaluate the structure of the output of the parser with average maximum path
length and average proportion of leaf nodes and find that the dependency trees
generated by the parser are close to the gold trees. As the corpus allows
non-projective structures, we analyze the complexity of non-projectivity of the
corpus and find that the dependency structures in this corpus have gap degree
at most one and edge degree at most one.

使用双仿射模型进行神经话语依存分析，与基线分析器相比实现了显著的性能提升。使用 Chu-Liu-Edmonds 算法生成的树更深，性能更好。虽然该语料库允许非项目结构，但分析了非项目性的复杂度并发现其依赖结构不超过一个间隔度和一个边缘度。

Biaffine 话语依存分析

Biaffine Discourse Dependency Parsing

Tagging based relational triple extraction methods are attracting growing
research attention recently. However, most of these methods take a
unidirectional extraction framework that first extracts all subjects and then
extracts objects and relations simultaneously based on the subjects extracted.
This framework has an obvious deficiency that it is too sensitive to the
extraction results of subjects. To overcome this deficiency, we propose a
bidirectional extraction framework based method that extracts triples based on
the entity pairs extracted from two complementary directions. Concretely, we
first extract all possible subject-object pairs from two paralleled directions.
These two extraction directions are connected by a shared encoder component,
thus the extraction features from one direction can flow to another direction
and vice versa. By this way, the extractions of two directions can boost and
complement each other. Next, we assign all possible relations for each entity
pair by a biaffine model. During training, we observe that the share structure
will lead to a convergence rate inconsistency issue which is harmful to
performance. So we propose a share-aware learning mechanism to address it. We
evaluate the proposed model on multiple benchmark datasets. Extensive
experimental results show that the proposed model is very effective and it
achieves state-of-the-art results on all of these datasets. Moreover,
experiments show that both the proposed bidirectional extraction framework and
the share-aware learning mechanism have good adaptability and can be used to
improve the performance of other tagging based methods. The source code of our
work is available at: this https URL

本文提出了一种基于双向抽取框架的标记关系三元组抽取方法，该方法在联合提取主体和关系的同时从两个方向抽取实体对，并通过共享编码器组件实现特征互补和提升，使用双仿射模型为每个实体对分配所有可能的关系，在多个基准数据集上验证其性能和有效性，此外，实验证明，所提出的双向抽取框架和共享感知学习机制具有很好的适应性，可用于提高其他标记基于方法的性能。

关系三元组抽取的简单而有效的双向框架

A Simple but Effective Bidirectional Framework for Relational Triple  Extraction

Named Entity Recognition (NER) is a fundamental task in Natural Language
Processing, concerned with identifying spans of text expressing references to
entities. NER research is often focused on flat entities only (flat NER),
ignoring the fact that entity references can be nested, as in [Bank of [China]]
(Finkel and Manning, 2009). In this paper, we use ideas from graph-based
dependency parsing to provide our model a global view on the input via a
biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of
start and end tokens in a sentence which we use to explore all spans, so that
the model is able to predict named entities accurately. We show that the model
works well for both nested and flat NER through evaluation on 8 corpora and
achieving SoTA performance on all of them, with accuracy gains of up to 2.2
percentage points.

本文基于图形依存解析思想，利用双仿射模型对输入进行全局视图，探索所有语句中的跨度，以准确预测命名实体，以 Nested NER 为例， 对 8 个语料库进行了评估并取得了所有语料库的 SOTA 性能，最高的准确度增益高达 2.2 个百分点。