One of the major goals in automated argumentation mining is to uncover the
argument structure present in argumentative text. In order to determine this
structure, one must understand how different individual components of the
overall argument are linked. General consensus in this field dictates that the
argument components form a hierarchy of persuasion, which manifests itself in a
tree structure. This work provides the first neural network-based approach to
argumentation mining, focusing on the two tasks of extracting links between
argument components, and classifying types of argument components. In order to
solve this problem, we propose to use a joint model that is based on a Pointer
Network architecture. A Pointer Network is appealing for this task for the
following reasons: 1) It takes into account the sequential nature of argument
components; 2) By construction, it enforces certain properties of the tree
structure present in argument relations; 3) The hidden representations can be
applied to auxiliary tasks. In order to extend the contribution of the original
Pointer Network model, we construct a joint model that simultaneously attempts
to learn the type of argument component, as well as continuing to predict links
between argument components. The proposed joint model achieves state-of-the-art
results on two separate evaluation corpora, achieving far superior performance
than a regular Pointer Network model. Our results show that optimizing for both
tasks, and adding a fully-connected layer prior to recurrent neural network
input, is crucial for high performance.

本文介绍了第一个基于神经网络的自动化论证挖掘方法，并采用了指针网络结构来提取和分类不同的论证组件，以揭示论证文本中存在的论证结构和层级体系。实验结果表明，优化两个任务的性能和添加完全连接层对于高性能至关重要。

联合指针体系结构用于论点挖掘

Here's My Point: Joint Pointer Architecture for Argument Mining

To enhance developer productivity, all modern integrated development
environments (IDEs) include code suggestion functionality that proposes likely
next tokens at the cursor. While current IDEs work well for statically-typed
languages, their reliance on type annotations means that they do not provide
the same level of support for dynamic programming languages as for
statically-typed languages. Moreover, suggestion engines in modern IDEs do not
propose expressions or multi-statement idiomatic code. Recent work has shown
that language models can improve code suggestion systems by learning from
software repositories. This paper introduces a neural language model with a
sparse pointer network aimed at capturing very long-range dependencies. We
release a large-scale code suggestion corpus of 41M lines of Python code
crawled from GitHub. On this corpus, we found standard neural language models
to perform well at suggesting local phenomena, but struggle to refer to
identifiers that are introduced many tokens in the past. By augmenting a neural
language model with a pointer network specialized in referring to predefined
classes of identifiers, we obtain a much lower perplexity and a 5 percentage
points increase in accuracy for code suggestion compared to an LSTM baseline.
In fact, this increase in code suggestion accuracy is due to a 13 times more
accurate prediction of identifiers. Furthermore, a qualitative analysis shows
this model indeed captures interesting long-range dependencies, like referring
to a class member defined over 60 tokens in the past.

本论文介绍一种神经语言模型，采用稀疏指针网络，以捕捉非常长的依赖关系，旨在提高 IDE 的代码建议系统的准确性，研究结果表明该模型相对于 LSTM 基线的代码建议准确率提高了 5 个百分点，得益于其 13 倍更准确的标识符预测功能。

使用稀疏指针网络学习 Python 代码建议

Learning Python Code Suggestion with a Sparse Pointer Network

Sentence ordering is one of important tasks in NLP. Previous works mainly
focused on improving its performance by using pair-wise strategy. However, it
is nontrivial for pair-wise models to incorporate the contextual sentence
information. In addition, error prorogation could be introduced by using the
pipeline strategy in pair-wise models. In this paper, we propose an end-to-end
neural approach to address the sentence ordering problem, which uses the
pointer network (Ptr-Net) to alleviate the error propagation problem and
utilize the whole contextual information. Experimental results show the
effectiveness of the proposed model. Source codes and dataset of this paper are
available.

本文提出一种端到端的神经方法来解决句子排序问题，该方法利用指针网络（Ptr-Net）以减轻错误传播问题，并利用整体上下文信息。实验结果表明了所提出模型的有效性。