论文标题
关于单词和句子表示在隐式话语关系分类中学习的重要性
On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
论文作者
论文摘要
隐式话语关系分类是浅说话解析中最困难的部分之一，因为没有明确的连接词的关系预测需要在文本跨度和句子级别上的语言理解。先前的研究主要集中于两个参数之间的相互作用。我们认为，强大的上下文表示表示模块，双边多观点匹配模块以及全局信息融合模块对于隐式话语分析都很重要。我们提出了一个新型模型，将这些模块结合在一起。广泛的实验表明，我们提出的模型在PDTB数据集上的表现优于BERT和其他最先进的系统，大约为8％，而Conll 2016数据集约为16％。我们还分析了不同模块在隐式话语关系分类任务中的有效性，并证明了不同级别的表示学习如何影响结果。
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.
