In a dialog, there can be multiple valid next utterances at any point. The
present end-to-end neural methods for dialog do not take this into account.
They learn with the assumption that at any time there is only one correct next
utterance. In this work, we focus on this problem in the goal-oriented dialog
setting where there are different paths to reach a goal. We propose a new
method, that uses a combination of supervised learning and reinforcement
learning approaches to address this issue. We also propose a new and more
effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid
next utterances to the original-bAbI dialog tasks, which allows evaluation of
goal-oriented dialog systems in a more realistic setting. We show that there is
a significant drop in performance of existing end-to-end neural methods from
81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on
permuted-bAbI dialog tasks. We also show that our proposed method improves the
performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog
tasks.

该研究工作关注基于目标导向的对话系统中的问题，提出了一种使用监督学习和强化学习方法的新方法，并引入了多个有效的下一个话语来评估具有更现实设置的目标导向性对话系统。通过引入多种有效的下一个话语，该文改进了原始 bAbI 对话任务，现有的端到端神经方法的表现从原始 bAbI 对话任务的 81.5% 下降到 permuted-bAbI 对话任务的 30.3%，而其提出的方法则取得了 47.3% 的准确度。

使用多个答案学习端到端面向目标的对话

Learning End-to-End Goal-Oriented Dialog with Multiple Answers

Goal-oriented dialog has been given attention due to its numerous
applications in artificial intelligence. Goal-oriented dialogue tasks occur
when a questioner asks an action-oriented question and an answerer responds
with the intent of letting the questioner know a correct action to take. To ask
the adequate question, deep learning and reinforcement learning have been
recently applied. However, these approaches struggle to find a competent
recurrent neural questioner, owing to the complexity of learning a series of
sentences. Motivated by theory of mind, we propose "Answerer in Questioner's
Mind" (AQM), a novel information theoretic algorithm for goal-oriented dialog.
With AQM, a questioner asks and infers based on an approximated probabilistic
model of the answerer. The questioner figures out the answerer's intention via
selecting a plausible question by explicitly calculating the information gain
of the candidate intentions and possible answers to each question. We test our
framework on two goal-oriented visual dialog tasks: "MNIST Counting Dialog" and
"GuessWhat?!". In our experiments, AQM outperforms comparative algorithms by a
large margin.

本研究提出了一种新的信息理论策略： 基于该策略的 AQM 模型能有效地处理面向目标的对话任务， 尤其是在问答者句子序列学习方面的复杂性，与其他相比，经验证明 AQm 更胜一筹。