# Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension

arXiv: https://arxiv.org/abs/1908.10721 · HTML: https://mlsanity.com/paper/1908.10721/

## Key facts

- Cited by 12 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [DAGN: Discourse-Aware Graph Network for Logical Reasoning](https://mlsanity.com/paper/2103.14349/) (2021)
- [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2210.07032/) (2022)
- [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://mlsanity.com/paper/2306.06480/) (2023)
- [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://mlsanity.com/paper/2110.03269/) (2021)
- [Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation](https://mlsanity.com/paper/2112.05364/) (2021)
- [Discourse-Aware Graph Networks for Textual Logical Reasoning](https://mlsanity.com/paper/2207.01450/) (2022)
- [Lite Unified Modeling for Discriminative Reading Comprehension](https://mlsanity.com/paper/2203.14103/) (2022)
- [A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysis](https://mlsanity.com/paper/2309.11849/) (2023)
- [Deep Understanding based Multi-Document Machine Reading Comprehension](https://mlsanity.com/paper/2204.03494/) (2022)
- [Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing](https://mlsanity.com/paper/2305.13755/) (2023)
- [A New Method for Cross-Lingual-based Semantic Role Labeling](https://mlsanity.com/paper/2408.15896/) (2024)

## Abstract (arXiv)

In this work, we propose to use linguistic annotations as a basis for a textitDiscourse-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.