# Enhancing Dialogue Systems with Discourse-Level Understanding Using Deep Canonical Correlation Analysis

arXiv: https://arxiv.org/abs/2504.09094 · HTML: https://mlsanity.com/paper/2504.09094/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://mlsanity.com/paper/1806.09102/) (2018)
- [Learning Natural Language Inference with LSTM](https://mlsanity.com/paper/1512.08849/) (2015)
- [Training Neural Response Selection for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.01543/) (2019)
- [Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/1901.01824/) (2019)
- [Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis](https://mlsanity.com/paper/1907.08696/) (2019)
- [Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis](https://mlsanity.com/paper/2406.12997/) (2024)

## Abstract (arXiv)

The evolution of conversational agents has been driven by the need for more contextually aware systems that can effectively manage dialogue over extended interactions. To address the limitations of existing models in capturing and utilizing long-term conversational history, we propose a novel framework that integrates Deep Canonical Correlation Analysis (DCCA) for discourse-level understanding. This framework learns discourse tokens to capture relationships between utterances and their surrounding context, enabling a better understanding of long-term dependencies. Experiments on the Ubuntu Dialogue Corpus demonstrate significant enhancement in response selection, based on the improved automatic evaluation metric scores. The results highlight the potential of DCCA in improving dialogue systems by allowing them to filter out irrelevant context and retain critical discourse information for more accurate response retrieval.