A good dialogue agent should have the ability to interact with users by both
responding to questions and by asking questions, and importantly to learn from
both types of interaction. In this work, we explore this direction by designing
a simulator and a set of synthetic tasks in the movie domain that allow such
interactions between a learner and a teacher. We investigate how a learner can
benefit from asking questions in both offline and online reinforcement learning
settings, and demonstrate that the learner improves when asking questions.
Finally, real experiments with Mechanical Turk validate the approach. Our work
represents a first step in developing such end-to-end learned interactive
dialogue agents.

利用机器人在电影领域的师生问答交互进行离线和在线强化学习，证明了学习者在提问时改进的效果，并验证了该方法。

通过对话互动提问学习

Learning through Dialogue Interactions by Asking Questions

A long-term goal of machine learning is to build intelligent conversational
agents. One recent popular approach is to train end-to-end models on a large
amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals
& Le, 2015; Shang et al., 2015). However, this approach leaves many questions
unanswered as an understanding of the precise successes and shortcomings of
each model is hard to assess. A contrasting recent proposal are the bAbI tasks
(Weston et al., 2015b) which are synthetic data that measure the ability of
learning machines at various reasoning tasks over toy language. Unfortunately,
those tests are very small and hence may encourage methods that do not scale.
In this work, we propose a suite of new tasks of a much larger scale that
attempt to bridge the gap between the two regimes. Choosing the domain of
movies, we provide tasks that test the ability of models to answer factual
questions (utilizing OMDB), provide personalization (utilizing MovieLens),
carry short conversations about the two, and finally to perform on natural
dialogs from Reddit. We provide a dataset covering 75k movie entities and with
3.5M training examples. We present results of various models on these tasks,
and evaluate their performance.

本文提出了基于电影领域的一套新任务套件，旨在测试模型回答事实性问题、提供个性化、进行短对话及在 Reddit 上执行自然对话的能力，评估了各种模型在这些任务上的性能表现。