Natural language understanding (NLU) and natural language generation (NLG)
are both critical research topics in the NLP field. Natural language
understanding is to extract the core semantic meaning from the given
utterances, while natural language generation is opposite, of which the goal is
to construct corresponding sentences based on the given semantics. However,
such dual relationship has not been investigated in the literature. This paper
proposes a new learning framework for language understanding and generation on
top of dual supervised learning, providing a way to exploit the duality. The
preliminary experiments show that the proposed approach boosts the performance
for both tasks.

该论文提出了一种基于双重监督学习的语言理解和生成学习框架，能够有效提高这两个任务的性能。

自然语言理解和生成的双重监督学习

Dual Supervised Learning for Natural Language Understanding and  Generation

Many supervised learning tasks are emerged in dual forms, e.g.,
English-to-French translation vs. French-to-English translation, speech
recognition vs. text to speech, and image classification vs. image generation.
Two dual tasks have intrinsic connections with each other due to the
probabilistic correlation between their models. This connection is, however,
not effectively utilized today, since people usually train the models of two
dual tasks separately and independently. In this work, we propose training the
models of two dual tasks simultaneously, and explicitly exploiting the
probabilistic correlation between them to regularize the training process. For
ease of reference, we call the proposed approach \emph{dual supervised
learning}. We demonstrate that dual supervised learning can improve the
practical performances of both tasks, for various applications including
machine translation, image processing, and sentiment analysis.

该研究提出一种被称为双重监督学习的方法，即同时训练两个双重形式的任务模型，并明确利用它们之间的概率关联来规范训练过程。该方法可同时提高多种应用程序的实际性能，包括机器翻译，图像处理和情感分析。