Existing benchmarks used to evaluate the performance of end-to-end neural
dialog systems lack a key component: natural variation present in human
conversations. Most datasets are constructed through crowdsourcing, where the
crowd workers follow a fixed template of instructions while enacting the role
of a user/agent. This results in straight-forward, somewhat routine, and mostly
trouble-free conversations, as crowd workers do not think to represent the full
range of actions that occur naturally with real users. In this work, we
investigate the impact of naturalistic variation on two goal-oriented datasets:
bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new
and more effective testbeds for both datasets, by introducing naturalistic
variation by the user. We observe that there is a significant drop in
performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on
bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet
and GLMP on both datasets.

探究自然变化对神经对话系统的影响，提出了两个考虑用户自然变化的新数据集，发现 BossNet 和 GLMP 等最新端到端神经方法在这些数据集上的表现严重下降。

目标导向对话中自然变化的影响

Effects of Naturalistic Variation in Goal-Oriented Dialog

This paper introduces zero-shot dialog generation (ZSDG), as a step towards
neural dialog systems that can instantly generalize to new situations with
minimal data. ZSDG enables an end-to-end generative dialog system to generalize
to a new domain for which only a domain description is provided and no training
dialogs are available. Then a novel learning framework, Action Matching, is
proposed. This algorithm can learn a cross-domain embedding space that models
the semantics of dialog responses which, in turn, lets a neural dialog
generation model generalize to new domains. We evaluate our methods on a new
synthetic dialog dataset, and an existing human-human dialog dataset. Results
show that our method has superior performance in learning dialog models that
rapidly adapt their behavior to new domains and suggests promising future
research.

该研究介绍了零 - shot 对话生成（ZSDG），以实现神经对话系统能够在提供最少数据的情况下即刻推广到新情景。该论文提出了一种新的学习框架 —— 动作匹配算法，能够学习跨领域嵌入空间，使神经对话生成模型能够推广到新的领域。结果显示我们的方法在学习对话模型方面具有优越性能，并具有良好的未来研究前景。

基于跨域潜在行动的零样本对话生成

Zero-Shot Dialog Generation with Cross-Domain Latent Actions

This paper addresses the question: Why do neural dialog systems generate
short and meaningless replies? We conjecture that, in a dialog system, an
utterance may have multiple equally plausible replies, causing the deficiency
of neural networks in the dialog application. We propose a systematic way to
mimic the dialog scenario in a machine translation system, and manage to
reproduce the phenomenon of generating short and less meaningful sentences in
the translation setting, showing evidence of our conjecture.

本文探讨神经对话系统为何会生成短小无意的回复。作者假设，在对话系统中，一句话可能有多种同样合理的回复，导致神经网络在对话应用中的不足。作者提出了一种系统的方法来模拟对话情境，成功在机器翻译系统中复现短小不具意义的句子生成现象，并展示了作者的假设的证据。

神经对话系统为何生成短且无意义的回复？对话和翻译的比较

Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A  Comparison between Dialog and Translation

The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of rules. However, incorporating personalization into such systems
is a largely unexplored topic as there are no existing corpora to facilitate
such work. In this paper, we present a new dataset of goal-oriented dialogs
which are influenced by speaker profiles attached to them. We analyze the
shortcomings of an existing end-to-end dialog system based on Memory Networks
and propose modifications to the architecture which enable personalization. We
also investigate personalization in dialog as a multi-task learning problem,
and show that a single model which shares features among various profiles
outperforms separate models for each profile.

本文提出了一种基于个人资料的目标导向对话数据集，结合修改内在神经系统结构、使用多任务学习的方法，将个性化应用于对话系统。