Speech Act Modeling of Written Asynchronous Conversations with Task-Specific Embeddings and Conditional Structured Models
Year: 2016
Type: conference-paper
Abstract: This paper addresses the problem of speech act recognition in written asynchronous conversations (e.g., fora, emails).We propose a class of conditional structured models defined over arbitrary graph structures to capture the conversational dependencies between sentences.Our models use sentence representations encoded by a long short term memory (LSTM) recurrent neural model.Empirical evaluation shows the effectiveness of our approach over existing ones: (i) LSTMs provide better task-specific representations, and (ii) the global joint model improves over local models.
DOI: 10.18653/v1/p16-1165
Authors: Shafiq Joty, Enamul Hoque
Institutions: Hamad bin Khalifa University, Qatar Foundation
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2517028602