DILiGENt: Domain-Independent Language Generation

我们提出了一个为期两年的项目，通过结合三个领域的最新进展，开发一种新的数据驱动方法，为新领域快速创建高质量的NLG系统：（1）NLG统计模型的进步，（2）自然语言数据收集的众包方法，这些方法在相关领域（如机器翻译）首次显示出有希望的结果，以及（3）最近开发的用于结构化预测的模仿学习算法。该项目团队结合了两个领先的研究小组在这些领域的专业知识：在赫瑞瓦特大学，我们最近展示了数据驱动的统计NLG在有限领域的潜力。为了使这个框架独立于领域，我们将利用最近的机器学习模型，由伦敦大学学院的研究人员开发。这些模型通过模仿人类专家将执行的动作来学习，以生成NL话语，我们通过紧密集成的众包程序收集这些话语。这项工作的成果是一个框架，它将允许NLG系统在新领域的快速发展，从而加速NLG技术对市场的影响。我们将在BBC提供的数据集上展示这个框架，在那里我们解决了为20，000多个地点生成天气报告的问题。目前，BBC网站上只有10篇气象学家撰写的报道。这些报告中的每一份都涉及该国相当大的地区（例如英格兰东部），因此通常对特定位置的天气感兴趣的用户几乎不感兴趣在第二个更雄心勃勃的步骤中，我们将探索这个框架如何扩展到更复杂的互动对话环境，其中生成必须考虑话语现象，例如长距离的话语关系或句法协调。这将在由Heriot-Watt大学主办的交互式系统中生成的共享任务挑战中进行评估。总之，该项目将进一步加深我们对领域无关语言生成的理解，并为该领域的未来研究提供大量新颖的资源（以代码和数据的形式），以及NLG系统在广泛领域的实际实现，从天气报告到自然语言界面。

We propose a two year project to develop a novel data-driven methodology to rapidly create high quality NLG systems for new domains, by combining recent advances in three domains: (1) advances in statistical models for NLG, (2) crowdsourcing methods for natural language data collection, which have shown first promising results in related fields, such as Machine Translation, and (3) recently developed imitation learning algorithms for structured prediction. The project team combines expertise of two leading research groups in these areas:At Heriot-Watt University, we recently demonstrated the potential for data-driven statistical NLG in limited domains. In order to make this framework domain-independent we will leverage recent machine learning models, developed by researchers at the University College London. These models learn by imitating the actions a human expert would perform to generate NL utterances, which we collect via a tightly integrated crowdsourcing procedure. The outcome of this work is a framework which will allow the rapid development of NLG systems for new domains, and thus accelerate the impact NLG technology has on the market. We will showcase this framework on a dataset provided by the BBC, where we address the problem of generating weather reports for over 20,000 individual locations. Currently, the BBC website features only 10 reports written by meteorologists. Each of these reports covers a rather large area of the country (e.g. East of England), and thus of little interest to their users who are usually interested in the weather in a particular location (e.g. Norwich).In a second, more ambitious step, we will explore how this framework scales to more complex interactive dialogue settings, where generation has to account for discourse phenomena, such as long-distance discourse relations or syntactic coordination. This will be evaluated in a shared task challenge for generation in interactive systems, hosted by Heriot-Watt University.In sum, this project will further our understanding of domain-independent language generation, as well as deliver substantial and novel resources to support future research in this area (in the forms of code and data), and practical implementations of NLG systems in a wide-range of domains, from weather reports to natural language interfaces.