# Joint Modeling of Content and Discourse Relations in Dialogues

arXiv: https://arxiv.org/abs/1705.05039 · HTML: https://mlsanity.com/paper/1705.05039/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2017.

## Built on by (most cited first, 6 of 6)

- [Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://mlsanity.com/paper/2104.08400/) (2021)
- [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022)
- [LARD: Large-scale Artificial Disfluency Generation](https://mlsanity.com/paper/2201.05041/) (2022)
- [Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues](https://mlsanity.com/paper/2302.05895/) (2023)
- [Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure](https://mlsanity.com/paper/2104.08601/) (2021)
- [TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization](https://mlsanity.com/paper/2204.04504/) (2022)

## Abstract (arXiv)

We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members' understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.