# Explaining Outcomes of Multi-Party Dialogues using Causal Learning

arXiv: https://arxiv.org/abs/2105.00944 · HTML: https://mlsanity.com/paper/2105.00944/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 2 of 2)

- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)

## Built on by (most cited first, 3 of 3)

- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation](https://mlsanity.com/paper/2407.03340/) (2024)
- [Explainable Rule Mining of IPv6 Extension-Header Presence Patterns from Paired-Vantage Captures](https://mlsanity.com/paper/2610.08090/) (2026)

## Abstract (arXiv)

Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics. The outcome of a conversation may be positive or negative. It is important to analyze why a dialogue ends with a particular sentiment from the point of view of conflict analysis as well as future collaboration design. We propose an explainable time series mining algorithm for such analysis. A dialogue is represented as an attributed time series of occurrences of keywords, EMPATH categories, and inferred sentiments at various points in its progress. A special decision tree, with decision metrics that take into account temporal relationships between dialogue events, is used for predicting the cause of the outcome sentiment. Interpretable rules mined from the classifier are used to explain the prediction. Experimental results are presented for the enterprise social media posts in a large company.