# Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/2406.12997 · HTML: https://mlsanity.com/paper/2406.12997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure](https://mlsanity.com/paper/2004.05080/) (2020)
- [Text Segmentation by Cross Segment Attention](https://mlsanity.com/paper/2004.14535/) (2020)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances](https://mlsanity.com/paper/2012.01775/) (2020)
- [Toward Fast and Accurate Neural Discourse Segmentation](https://mlsanity.com/paper/1808.09147/) (2018)
- [Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading](https://mlsanity.com/paper/2009.01303/) (2020)
- [Representation Learning for Conversational Data using Discourse Mutual Information Maximization](https://mlsanity.com/paper/2112.05787/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents](https://mlsanity.com/paper/2504.09073/) (2025)
- [Enhancing Dialogue Systems with Discourse-Level Understanding Using Deep Canonical Correlation Analysis](https://mlsanity.com/paper/2504.09094/) (2025)

## Abstract (arXiv)

Canonical Correlation Analysis (CCA) has been exploited immensely for learning latent representations in various fields. This study takes a step further by demonstrating the potential of CCA in identifying Elementary Discourse Units(EDUs) that captures the latent information within the textual data. The probabilistic interpretation of CCA discussed in this study utilizes the two-view nature of textual data, i.e. the consecutive sentences in a document or turns in a dyadic conversation, and has a strong theoretical foundation. Furthermore, this study proposes a model for Elementary Discourse Unit(EDU) segmentation that discovers EDUs in textual data without any supervision. To validate the model, the EDUs are utilized as textual unit for content selection in textual similarity task. Empirical results on Semantic Textual Similarity(STSB) and Mohler datasets confirm that, despite represented as a unigram, the EDUs deliver competitive results and can even beat various sophisticated supervised techniques. The model is simple, linear, adaptable and language independent making it an ideal baseline particularly when labeled training data is scarce or nonexistent.