Paper Info
Title
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation.
Abstract
We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled conversational data and the available labeled data from synchronous domains. We carry out our research in three main steps. First, we introduce a neural architecture based on hierarchical LSTMs and conditional random fields (CRF) for SAR, and show that our method outperforms existing methods when trained on in-domain data only. Second, we improve our initial SAR models by semi-supervised learning in the form of pretrained word embeddings learned from a large unlabeled conversational corpus. Finally, we employ adversarial training to improve the results further by leveraging the labeled data from synchronous domains and by explicitly modeling the distributional shift in two domains.
Year
Venue
Field
2019
arXiv: Computation and Language
Asynchronous communication,Conversation,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Speech act
DocType
Volume
Citations 
Journal
abs/1904.04021
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Tasnim Mohiuddin102.70
Thanh-Tung Nguyen2193.10
Shafiq R. Joty356056.72