---
title: "Unsupervised Modeling of Dialog Acts in Asynchronous Conversations"
authors:
  - "Shafiq R. Joty"
  - "Giuseppe Carenini"
  - "Chin-Yew Lin"
doi: "10.5591/978-1-57735-516-8/IJCAI11-303"
url: "https://ijcai.org/proceedings/2011/303"
pdf: "https://ijcai.org/proceedings/2011/0303.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1807-1812"
track: ""
keywords:
---

# Unsupervised Modeling of Dialog Acts in Asynchronous Conversations

Shafiq R. Joty, Giuseppe Carenini, Chin-Yew Lin

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1807-1812. DOI: [10.5591/978-1-57735-516-8/IJCAI11-303](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-303)

## Abstract

We present unsupervised approaches to the problem of modeling dialog acts in asynchronous conversations; i.e., conversations where participants collaborate with each other at different times. In particular, we investigate a graph-theoretic deterministic framework and two probabilistic conversation models (i.e., HMM and HMM+Mix) for modeling dialog acts in emails and forums. We train and test our conversation models on (a) temporal order and (b) graph-structural order of the datasets. Empirical evaluation suggests (i) the graph-theoretic framework that relies on lexical and structural similarity metrics is not the right model for this task, (ii) conversation models perform better on the graph-structural order than the temporal order of the datasets and (iii) HMM+Mix is a better conversation model than the simple HMM model.