# Structural Characterization for Dialogue Disentanglement

arXiv: https://arxiv.org/abs/2110.08018 · HTML: https://mlsanity.com/paper/2110.08018/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 66 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension](https://mlsanity.com/paper/1902.00164/) (2019)
- [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://mlsanity.com/paper/2004.05388/) (2020)
- [Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2004.03588/) (2020)
- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [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)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue](https://mlsanity.com/paper/2009.06504/) (2020)
- [Response Selection for Multi-Party Conversations with Dynamic Topic Tracking](https://mlsanity.com/paper/2010.07785/) (2020)
- [Multi-turn Response Selection using Dialogue Dependency Relations](https://mlsanity.com/paper/2010.01502/) (2020)
- [Better Feature Integration for Named Entity Recognition](https://mlsanity.com/paper/2104.05316/) (2021)
- [Structural Pre-training for Dialogue Comprehension](https://mlsanity.com/paper/2105.10956/) (2021)
- [Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer](https://mlsanity.com/paper/1911.10666/) (2019)
- [DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension](https://mlsanity.com/paper/2104.12377/) (2021)
- [Online Conversation Disentanglement with Pointer Networks](https://mlsanity.com/paper/2010.11080/) (2020)
- [DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues](https://mlsanity.com/paper/2012.02553/) (2020)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement](https://mlsanity.com/paper/2004.03760/) (2020)

## Built on by (most cited first, 7 of 7)

- [EM Pre-training for Multi-party Dialogue Response Generation](https://mlsanity.com/paper/2305.12412/) (2023)
- [Conversation Disentanglement with Bi-Level Contrastive Learning](https://mlsanity.com/paper/2210.15265/) (2022)
- [Pre-training Multi-party Dialogue Models with Latent Discourse Inference](https://mlsanity.com/paper/2305.15175/) (2023)
- [Dramatic Conversation Disentanglement](https://mlsanity.com/paper/2305.16648/) (2023)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [Thread Detection and Response Generation using Transformers with Prompt Optimisation](https://mlsanity.com/paper/2403.05931/) (2024)
- [Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension](https://mlsanity.com/paper/2305.08348/) (2023)

## Abstract (arXiv)

Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, increasing difficulties in understanding the dialogue history for both human and machine. Previous studies mainly focus on utterance encoding methods with carefully designed features but pay inadequate attention to characteristic features of the structure of dialogues. We specially take structure factors into account and design a novel model for dialogue disentangling. Based on the fact that dialogues are constructed on successive participation and interactions between speakers, we model structural information of dialogues in two aspects: 1)speaker property that indicates whom a message is from, and 2) reference dependency that shows whom a message may refer to. The proposed method achieves new state-of-the-art on the Ubuntu IRC benchmark dataset and contributes to dialogue-related comprehension.