In this work, we propose to use linguistic annotations as a basis for a
\textit{Discourse-Aware Semantic Self-Attention} encoder that we employ for
reading comprehension on long narrative texts. We extract relations between
discourse units, events and their arguments as well as coreferring mentions,
using available annotation tools. Our empirical evaluation shows that the
investigated structures improve the overall performance, especially
intra-sentential and cross-sentential discourse relations, sentence-internal
semantic role relations, and long-distance coreference relations. We show that
dedicating self-attention heads to intra-sentential relations and relations
connecting neighboring sentences is beneficial for finding answers to questions
in longer contexts. Our findings encourage the use of discourse-semantic
annotations to enhance the generalization capacity of self-attention models for
reading comprehension.

使用语言学注释作为基础，提出结构改进并应用于长篇叙述文本的阅读理解中，通过提取段落单元之间的关系、事件与其参数以及代词提及之间的关系来提高模型的性能，尤其是在语境内部的语义角色关系、句内关系、长距离代词关系中。实验结果表明，在需考虑到句内、句间关系的长文本中，将注意力集中在这些关系上可以提高模型的性能。研究结果表明，使用语篇 - 语义注释可以增强自注意力模型在阅读理解中的泛化能力。