A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models
This addresses the issue of controlling personality and knowledge consistency in chatbots for movie discussions, but it is incremental as it focuses on a specific domain with new data.
The authors tackled the problem of inconsistent behavior in chatbots by introducing a new labeled dialogue dataset for movie discussions, where each dialogue is based on pre-specified facts and opinions, and they showed that a baseline model trained on this data generates responses judged as natural, knowledgeable, and attentive.
Fully data driven Chatbots for non-goal oriented dialogues are known to suffer from inconsistent behaviour across their turns, stemming from a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. One reason for this is the relative lack of labeled data from which personality consistency and fact usage could be learned together with dialogue behaviour. To address this, we introduce a new labeled dialogue dataset in the domain of movie discussions, where every dialogue is based on pre-specified facts and opinions. We thoroughly validate the collected dialogue for adherence of the participants to their given fact and opinion profile, and find that the general quality in this respect is high. This process also gives us an additional layer of annotation that is potentially useful for training models. We introduce as a baseline an end-to-end trained self-attention decoder model trained on this data and show that it is able to generate opinionated responses that are judged to be natural and knowledgeable and show attentiveness.