We propose Machines Talking To Machines (M2M), a framework combining
automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents
for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with
just a task schema and an API client from the dialogue system developer, but it
is also customizable to cater to task-specific interactions. Compared to the
Wizard-of-Oz approach for data collection, M2M achieves greater diversity and
coverage of salient dialogue flows while maintaining the naturalness of
individual utterances. In the first phase, a simulated user bot and a
domain-agnostic system bot converse to exhaustively generate dialogue
"outlines", i.e. sequences of template utterances and their semantic parses. In
the second phase, crowd workers provide contextual rewrites of the dialogues to
make the utterances more natural while preserving their meaning. The entire
process can finish within a few hours. We propose a new corpus of 3,000
dialogues spanning 2 domains collected with M2M, and present comparisons with
popular dialogue datasets on the quality and diversity of the surface forms and
dialogue flows.

提出了基于自动化和众包的 Machines Talking To Machines (M2M) 框架，可用于创建面向目标对话的端到端对话代理，该框架可适应特定任务，并能够收集更多不同领域内的对话流，并维持对话时的自然度，同时，提供了一个新的对话语料库，包含 3000 次对话，涵盖了 2 个领域，与流行的对话数据集进行了比较。

使用对话自我博弈，一夜之间构建一个交流代理人

Building a Conversational Agent Overnight with Dialogue Self-Play

One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks in deep neural networks. This method was evaluated in
within-corpus and various cross-corpus classification experiments that simulate
conditions "in the wild". In comparison to Single-Task Learning (STL) based
state of the art methods, we found that our MTL method proposed improved
performance significantly. Particularly, models using both gender and
naturalness achieved more gains than those using either gender or naturalness
separately. This benefit was also found in the high-level representations of
the feature space, obtained from our method proposed, where discriminative
emotional clusters could be observed.

使用多任务学习以及在深度神经网络中使用性别和自然度等辅助任务来改善情感模型的泛化能力，并在理论模拟和实际数据中比较了单任务学习方法，结果表明多任务学习方法显著提高了情感识别性能，尤其是使用性别和自然度都提高了性能。

采用聚合语料库和深度多任务学习实现野外情感识别语音

Towards Speech Emotion Recognition "in the wild" using Aggregated  Corpora and Deep Multi-Task Learning

Despite the substantial progress in recent years, the image captioning
techniques are still far from being perfect.Sentences produced by existing
methods, e.g. those based on RNNs, are often overly rigid and lacking in
variability. This issue is related to a learning principle widely used in
practice, that is, to maximize the likelihood of training samples. This
principle encourages high resemblance to the "ground-truth" captions while
suppressing other reasonable descriptions. Conventional evaluation metrics,
e.g. BLEU and METEOR, also favor such restrictive methods. In this paper, we
explore an alternative approach, with the aim to improve the naturalness and
diversity -- two essential properties of human expression. Specifically, we
propose a new framework based on Conditional Generative Adversarial Networks
(CGAN), which jointly learns a generator to produce descriptions conditioned on
images and an evaluator to assess how well a description fits the visual
content. It is noteworthy that training a sequence generator is nontrivial. We
overcome the difficulty by Policy Gradient, a strategy stemming from
Reinforcement Learning, which allows the generator to receive early feedback
along the way. We tested our method on two large datasets, where it performed
competitively against real people in our user study and outperformed other
methods on various tasks.

该研究旨在通过使用基于条件生成对抗网络（CGAN）的新框架来提高图像字幕生成的自然性和多样性，其中一个生成器可以根据图像生成描述，并使用一个评估器来评估描述与视觉内容的匹配程度。