Difference between revisions of "Unsupervised Modeling of Dialog Acts in Asynchronous Conversation"
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== Online version == | == Online version == | ||
| − | [http://www.cs.ubc.ca/~rjoty/paper/ijcai11CR-Joty.pdf | + | [http://www.cs.ubc.ca/~rjoty/paper/ijcai11CR-Joty.pdf Click here to download] |
== Brief Summary== | == Brief Summary== | ||
This [[Category::paper]] aims at [[AddressesProblem::Modeling of Dialog Acts]] in asynchronous conversations in an unsupervised setting. There were 12 different dialog acts targeted, viz. Statement, Polite Mechanism, Yes-no question, Action motivator, Wh-question, Accept response, Open-ended question, Acknowledge and appreciate, Or-clause question, Reject response, Uncertain response, and Rhetorical Question. The experiments were done on conversations from two domains: emails, and discussion fora. | This [[Category::paper]] aims at [[AddressesProblem::Modeling of Dialog Acts]] in asynchronous conversations in an unsupervised setting. There were 12 different dialog acts targeted, viz. Statement, Polite Mechanism, Yes-no question, Action motivator, Wh-question, Accept response, Open-ended question, Acknowledge and appreciate, Or-clause question, Reject response, Uncertain response, and Rhetorical Question. The experiments were done on conversations from two domains: emails, and discussion fora. | ||
The authors started with modeling the problem as a clustering problem. They used a graph theoretic framework and represented the conversation as a Fragment Quotation Graph (FQG), in which each email fragment or forum post was represented as a node, and an edge existed between two nodes if one fragment or post was in response to the other. The weights on the edges were decided using a number of features which we shall see later. An N min cut was then used to cluster the graphs. However, this experiment didn't prove to be doing well. The authors took specific measures so as to avoid topic-based clustering, but the model was still confusing dialog-acts with the topics. The authors, thus, resorted to HMM so that they could make use of the sequential structure of the conversations. However, based on the experiments with clustering they were apprehensive if they could separate topic-modeling from dialog-act-modeling even when HMM is used. So they tried a combination of HMM and Gaussian Mixtures to model the dialog acts. The final results beat the baseline by a significant margin. | The authors started with modeling the problem as a clustering problem. They used a graph theoretic framework and represented the conversation as a Fragment Quotation Graph (FQG), in which each email fragment or forum post was represented as a node, and an edge existed between two nodes if one fragment or post was in response to the other. The weights on the edges were decided using a number of features which we shall see later. An N min cut was then used to cluster the graphs. However, this experiment didn't prove to be doing well. The authors took specific measures so as to avoid topic-based clustering, but the model was still confusing dialog-acts with the topics. The authors, thus, resorted to HMM so that they could make use of the sequential structure of the conversations. However, based on the experiments with clustering they were apprehensive if they could separate topic-modeling from dialog-act-modeling even when HMM is used. So they tried a combination of HMM and Gaussian Mixtures to model the dialog acts. The final results beat the baseline by a significant margin. | ||
Revision as of 14:23, 5 October 2011
Citation
Shafiq Joty, Giuseppe Carenini, Chin-Yew Lin. Unsupervised Modeling of Dialog Acts in Asynchronous Conversations. In Proceedings of the twenty second International Joint Conference on Artificial Intelligence (IJCAI) 2011. Barcelona, Spain.
Online version
Brief Summary
This paper aims at Modeling of Dialog Acts in asynchronous conversations in an unsupervised setting. There were 12 different dialog acts targeted, viz. Statement, Polite Mechanism, Yes-no question, Action motivator, Wh-question, Accept response, Open-ended question, Acknowledge and appreciate, Or-clause question, Reject response, Uncertain response, and Rhetorical Question. The experiments were done on conversations from two domains: emails, and discussion fora. The authors started with modeling the problem as a clustering problem. They used a graph theoretic framework and represented the conversation as a Fragment Quotation Graph (FQG), in which each email fragment or forum post was represented as a node, and an edge existed between two nodes if one fragment or post was in response to the other. The weights on the edges were decided using a number of features which we shall see later. An N min cut was then used to cluster the graphs. However, this experiment didn't prove to be doing well. The authors took specific measures so as to avoid topic-based clustering, but the model was still confusing dialog-acts with the topics. The authors, thus, resorted to HMM so that they could make use of the sequential structure of the conversations. However, based on the experiments with clustering they were apprehensive if they could separate topic-modeling from dialog-act-modeling even when HMM is used. So they tried a combination of HMM and Gaussian Mixtures to model the dialog acts. The final results beat the baseline by a significant margin.
