Implicit discourse relation classification is one of the most difficult parts
in shallow discourse parsing as the relation prediction without explicit
connectives requires the language understanding at both the text span level and
the sentence level. Previous studies mainly focus on the interactions between
two arguments. We argue that a powerful contextualized representation module, a
bilateral multi-perspective matching module, and a global information fusion
module are all important to implicit discourse analysis. We propose a novel
model to combine these modules together. Extensive experiments show that our
proposed model outperforms BERT and other state-of-the-art systems on the PDTB
dataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze the
effectiveness of different modules in the implicit discourse relation
classification task and demonstrate how different levels of representation
learning can affect the results.

我们提出了一种将强大的上下文表示模块，双边多角度匹配模块和全局信息融合模块结合的新型模型，用于隐含语篇关系分类任务，并在 PDTB 数据集上比 BERT 和其他最先进系统的表现提高了约 8％，在 CoNLL 2016 数据集上提高了约 16％。我们还分析了不同模块在隐含语篇关系分类任务中的有效性，并展示了不同表示学习层次如何影响结果。

探讨隐含语篇关系分类中词语和句子表征学习的重要性

On the Importance of Word and Sentence Representation Learning in  Implicit Discourse Relation Classification

Recognizing implicit discourse relations is a challenging but important task
in the field of Natural Language Processing. For such a complex text processing
task, different from previous studies, we argue that it is necessary to
repeatedly read the arguments and dynamically exploit the efficient features
useful for recognizing discourse relations. To mimic the repeated reading
strategy, we propose the neural networks with multi-level attention (NNMA),
combining the attention mechanism and external memories to gradually fix the
attention on some specific words helpful to judging the discourse relations.
Experiments on the PDTB dataset show that our proposed method achieves the
state-of-art results. The visualization of the attention weights also
illustrates the progress that our model observes the arguments on each level
and progressively locates the important words.

本文通过结合注意力机制和外部记忆的神经网络，提出了多级关注度（NNMA）方法，用于识别隐含的言语关系。实验结果证明，本方法在 PDTB 数据集方面达到了最先进的成果，并且注意权重的可视化揭示了模型在逐渐固定关注度并定位重要词汇的过程。