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Related Papers
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
AbstractThe majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In …...
What makes a good conversation? How controllable attributes affect human judgments
AbstractA good conversation requires balance – between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments …...
Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback
AbstractThe ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by …...
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
AbstractWe explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra modules to …...
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation
AbstractDialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses. In this paper, we propose a new dialog …...
How do decoding algorithms distribute information in dialogue responses?
AbstractHumans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID principle, and under what conditions …...