# Predicting Discourse Structure using Distant Supervision from Sentiment

arXiv: https://arxiv.org/abs/1910.14176 · HTML: https://mlsanity.com/paper/1910.14176/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices](https://mlsanity.com/paper/2112.03572/) (2021)
- [Predicting Discourse Trees from Transformer-based Neural Summarizers](https://mlsanity.com/paper/2104.07058/) (2021)
- [Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation](https://mlsanity.com/paper/2112.06196/) (2021)
- [Improving Topic Segmentation by Injecting Discourse Dependencies](https://mlsanity.com/paper/2209.08626/) (2022)
- [Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues](https://mlsanity.com/paper/2302.05895/) (2023)
- [Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set](https://mlsanity.com/paper/2503.10515/) (2025)
- [Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder](https://mlsanity.com/paper/2210.09559/) (2022)
- [Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing](https://mlsanity.com/paper/2305.13755/) (2023)

## Abstract (arXiv)

Discourse parsing could not yet take full advantage of the neural NLP revolution, mostly due to the lack of annotated datasets. We propose a novel approach that uses distant supervision on an auxiliary task (sentiment classification), to generate abundant data for RST-style discourse structure prediction. Our approach combines a neural variant of multiple-instance learning, using document-level supervision, with an optimal CKY-style tree generation algorithm. In a series of experiments, we train a discourse parser (for only structure prediction) on our automatically generated dataset and compare it with parsers trained on human-annotated corpora (news domain RST-DT and Instructional domain). Results indicate that while our parser does not yet match the performance of a parser trained and tested on the same dataset (intra-domain), it does perform remarkably well on the much more difficult and arguably more useful task of inter-domain discourse structure prediction, where the parser is trained on one domain and tested/applied on another one.