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DILiGENt: Domain-Independent Language Generation

DILiGENt：与领域无关的语言生成

基本信息

批准号：
EP/M005429/1
负责人：
Verena Rieser
金额：
$ 57.8万
依托单位：
Heriot-Watt University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM005429%2F1
关键词：
DILiGENt Domain Independent Language Generation

项目摘要

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.

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的方法，为新的领域快速创建高质量的NLG系统：(1)NLG统计模型的进展，(2)自然语言数据收集的众包方法，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研究小组在这些领域的专业知识：在赫里奥特-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，我们将利用由伦敦大学学院的研究人员开发的最新机器学习模型。这些模型通过模仿人类专家生成自然语言话语的行为来学习，我们通过紧密集成的众包程序收集这些话语。这项工作的结果是一个框架，它将允许NLG系统针对新领域的快速开发，从而加速NLG技术对市场的影响。我们将在英国广播公司提供的数据集上展示这个框架，在那里我们解决了为20,000多个单独地点生成天气报告的问题。目前，BBC网站上只有10篇由气象学家撰写的报告。这些报告中的每一个都覆盖了英国相当大的区域(例如英格兰东部)，因此通常对特定地点(例如诺里奇)天气感兴趣的用户并不感兴趣。在第二个更雄心勃勃的步骤中，我们将探索该框架如何扩展到更复杂的交互对话环境，其中生成必须考虑话语现象，如长距离话语关系或句法协调。这将在Heriot-Watt大学主办的交互式系统生成的共享任务挑战中进行评估。总而言之，该项目将加深我们对独立于领域的语言生成的理解，并提供大量和新颖的资源，以支持这一领域的未来研究(以代码和数据的形式)，以及NLG系统在从天气预报到自然语言界面等广泛领域的实际实施。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A review of evaluation techniques for social dialogue systems

DOI：
10.1145/3139491.3139504
发表时间：
2017-08
期刊：
Proceedings of the 1st ACM SIGCHI International Workshop on Investigating Social Interactions with Artificial Agents
影响因子：
0
作者：
A. C. Curry;H. Hastie;Verena Rieser
通讯作者：
A. C. Curry;H. Hastie;Verena Rieser

A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents