DAL combines dual learning on query-response pairs with adversarial training to improve diversity and naturalness in generated dialogue responses over prior methods.
An Information Retrieval Approach to Short Text Conversation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.
fields
cs.CL 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
DAL: Dual Adversarial Learning for Dialogue Generation
DAL combines dual learning on query-response pairs with adversarial training to improve diversity and naturalness in generated dialogue responses over prior methods.