# A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents

arXiv: https://arxiv.org/abs/2504.09073 · HTML: https://mlsanity.com/paper/2504.09073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (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)
- [Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure](https://mlsanity.com/paper/2004.05080/) (2020)
- [KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://mlsanity.com/paper/2004.04100/) (2020)
- [Towards Exploiting Background Knowledge for Building Conversation Systems](https://mlsanity.com/paper/1809.08205/) (2018)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [Hierarchical Recurrent Attention Network for Response Generation](https://mlsanity.com/paper/1701.07149/) (2017)
- [A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions](https://mlsanity.com/paper/1906.00500/) (2019)
- [Structured Attention for Unsupervised Dialogue Structure Induction](https://mlsanity.com/paper/2009.08552/) (2020)
- [Modeling Long Context for Task-Oriented Dialogue State Generation](https://mlsanity.com/paper/2004.14080/) (2020)
- [Fine-Grained Sentence Functions for Short-Text Conversation](https://mlsanity.com/paper/1907.10302/) (2019)
- [Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis](https://mlsanity.com/paper/2406.12997/) (2024)

## Abstract (arXiv)

Multiturn dialogue models aim to generate human-like responses by leveraging conversational context, consisting of utterances from previous exchanges. Existing methods often neglect the interactions between these utterances or treat all of them as equally significant. This paper introduces a discourse-aware framework for response selection in retrieval-based dialogue systems. The proposed model first encodes each utterance and response with contextual, positional, and syntactic features using Multi-view Canonical Correlation Analysis (MCCA). It then learns discourse tokens that capture relationships between an utterance and its surrounding turns in a shared subspace via Canonical Correlation Analysis (CCA). This two-step approach effectively integrates semantic and syntactic features to build discourse-level understanding. Experiments on the Ubuntu Dialogue Corpus demonstrate that our model achieves significant improvements in automatic evaluation metrics, highlighting its effectiveness in response selection.