Comprehensive Conditioning of Neural Conversational Models
MTA SZTAKI (Lágymányosi u. 11, Budapest) Room 306
Neural network based approaches to conversational modeling have been prevalent in the last three years. While there have been a multitude of techniques proposed in order to augment the performance of dialog agents, open-domain chatbots still tend to produce generic and safe responses, without much diversity [Li et al., 2015, Vinyals and Le, 2015]. This is caused by the learning target not being well-defined for training conversational models. In my work I plan to explore ideas meant to address this issue. Namely, building dialog models that are conditioned on more prior information, like persona, mood, world-knowledge, and outside factors. This should, in theory, ensure that the models do not simply average out ambiguities in the dataset, and thus a more natural and diverse chatbot could be created.