# Improving Multi-Party Dialogue Discourse Parsing via Domain Integration

arXiv: https://arxiv.org/abs/2110.04526 · HTML: https://mlsanity.com/paper/2110.04526/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure](https://mlsanity.com/paper/2004.05080/) (2020)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [Transfer Learning with Dynamic Adversarial Adaptation Network](https://mlsanity.com/paper/1909.08184/) (2019)
- [Multilingual Neural RST Discourse Parsing](https://mlsanity.com/paper/2012.01704/) (2020)

## Built on by (most cited first, 14 of 14)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study](https://mlsanity.com/paper/2305.08391/) (2023)
- [Multi-Party Conversational Agents: A Survey](https://mlsanity.com/paper/2505.18845/) (2025)
- [Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues](https://mlsanity.com/paper/2302.05895/) (2023)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [Llamipa: An Incremental Discourse Parser](https://mlsanity.com/paper/2406.18256/) (2024)
- [Leveraging Discourse Structure for Extractive Meeting Summarization](https://mlsanity.com/paper/2405.11055/) (2024)
- [Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue](https://mlsanity.com/paper/2405.19799/) (2024)
- [Multilingual Coreference Resolution in Multiparty Dialogue](https://mlsanity.com/paper/2208.01307/) (2022)
- [M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/2203.16799/) (2022)
- [Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension](https://mlsanity.com/paper/2305.08348/) (2023)
- [CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations](https://mlsanity.com/paper/2506.08504/) (2025)
- [Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts](https://mlsanity.com/paper/2606.27233/) (2026)
- [Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing](https://mlsanity.com/paper/2607.01964/) (2026)

## Abstract (arXiv)

While multi-party conversations are often less structured than monologues and documents, they are implicitly organized by semantic level correlations across the interactive turns, and dialogue discourse analysis can be applied to predict the dependency structure and relations between the elementary discourse units, and provide feature-rich structural information for downstream tasks. However, the existing corpora with dialogue discourse annotation are collected from specific domains with limited sample sizes, rendering the performance of data-driven approaches poor on incoming dialogues without any domain adaptation. In this paper, we first introduce a Transformer-based parser, and assess its cross-domain performance. We next adopt three methods to gain domain integration from both data and language modeling perspectives to improve the generalization capability. Empirical results show that the neural parser can benefit from our proposed methods, and performs better on cross-domain dialogue samples.