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

We propose a new task of unsupervised action detection by action matching.
Given two long videos, the objective is to temporally detect all pairs of
matching video segments. A pair of video segments are matched if they share the
same human action. The task is category independent---it does not matter what
action is being performed---and no supervision is used to discover such video
segments. Unsupervised action detection by action matching allows us to align
videos in a meaningful manner. As such, it can be used to discover new action
categories or as an action proposal technique within, say, an action detection
pipeline. Moreover, it is a useful pre-processing step for generating video
highlights, e.g., from sports videos.
We present an effective and efficient method for unsupervised action
detection. We use an unsupervised temporal encoding method and exploit the
temporal consistency in human actions to obtain candidate action segments. We
evaluate our method on this challenging task using three activity recognition
benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action
detection benchmark and a new dataset called the IKEA dataset. On the MPII
Cooking dataset we detect action segments with a precision of 21.6% and recall
of 11.7% over 946 long video pairs and over 5000 ground truth action segments.
Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over
5094 ground truth action segment pairs.

本文提出了一种新的无监督行为检测方法，通过行为匹配检测共享相同人类行为的视频片段，该方法可用于发现新的行为类别、作为活动检测流程中的行为提议技术，同时还是生成视频重点的有用预处理步骤，作者在 MPII Cooking 数据集、THUMOS15 行为检测基准和 IKEA 数据集上使用三个活动识别基准评估了该方法。