# Generic Dependency Modeling for Multi-Party Conversation

arXiv: https://arxiv.org/abs/2302.10680 · HTML: https://mlsanity.com/paper/2302.10680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 21 Feb 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020)
- [Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure](https://mlsanity.com/paper/2004.05080/) (2020)
- [M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.02187/) (2022)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [Tree-structured Attention with Hierarchical Accumulation](https://mlsanity.com/paper/2002.08046/) (2020)
- [GSN: A Graph-Structured Network for Multi-Party Dialogues](https://mlsanity.com/paper/1905.13637/) (2019)
- [Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue](https://mlsanity.com/paper/2009.06504/) (2020)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation](https://mlsanity.com/paper/2412.05342/) (2024)

## Abstract (arXiv)

To model the dependencies between utterances in multi-party conversations, we propose a simple and generic framework based on the dependency parsing results of utterances. Particularly, we present an approach to encoding the dependencies in the form of relative dependency encoding (ReDE) and illustrate how to implement it in Transformers by modifying the computation of self-attention. Experimental results on four multi-party conversation benchmarks show that this framework successfully boosts the general performance of two Transformer-based language models and leads to comparable or even superior performance compared to the state-of-the-art methods. The codes are available at https://github.com/shenwzh3/ReDE.