# Online Conversation Disentanglement with Pointer Networks

arXiv: https://arxiv.org/abs/2010.11080 · HTML: https://mlsanity.com/paper/2010.11080/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

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

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising](https://mlsanity.com/paper/2105.07911/) (2021)
- [Unsupervised Conversation Disentanglement through Co-Training](https://mlsanity.com/paper/2109.03199/) (2021)
- [Structural Characterization for Dialogue Disentanglement](https://mlsanity.com/paper/2110.08018/) (2021)
- [Conversation Disentanglement with Bi-Level Contrastive Learning](https://mlsanity.com/paper/2210.15265/) (2022)
- [On the Capabilities of Pointer Networks for Deep Deductive Reasoning](https://mlsanity.com/paper/2106.09225/) (2021)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection](https://mlsanity.com/paper/2110.12646/) (2021)
- [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)
- [Disentangling Online Chats with DAG-Structured LSTMs](https://mlsanity.com/paper/2106.09024/) (2021)
- [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://mlsanity.com/paper/2303.05400/) (2023)
- [Multi-party Response Generation with Relation Disentanglement](https://mlsanity.com/paper/2403.10827/) (2024)

## Abstract (arXiv)

Huge amounts of textual conversations occur online every day, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. However, existing disentanglement methods rely mostly on handcrafted features that are dataset specific, which hinders generalization and adaptability. In this work, we propose an end-to-end online framework for conversation disentanglement that avoids time-consuming domain-specific feature engineering. We design a novel way to embed the whole utterance that comprises timestamp, speaker, and message text, and proposes a custom attention mechanism that models disentanglement as a pointing problem while effectively capturing inter-utterance interactions in an end-to-end fashion. We also introduce a joint-learning objective to better capture contextual information. Our experiments on the Ubuntu IRC dataset show that our method achieves state-of-the-art performance in both link and conversation prediction tasks.