NSF-BSF: Collaborative Research: RI: Small: Multilingual Language Generation via Understanding of Code Switching

人类语言技术最近已经成熟到这样的程度：计算系统通常可以以对人类来说很自然的方式与用户交互，而不仅仅是对机器。然而，当今世界上大多数人都使用多种语言，目前的语言技术方法并没有反映出多语言交流无处不在的现实；也就是说，目前的技术可以自然地与单语使用者互动，但不能与多语使用者互动。计算系统应该能够生成对这些用户来说听起来同样自然的语言，这包括能够容纳非母语人士。这个项目首先创建了一个大规模的、广泛覆盖的数据集，反映了人类和一个自动系统之间的对话，这个系统足够复杂，可以生成流利的多语言（即英语）。“代码转换”)的话语，但对于对照实验来说足够简单。该数据集比目前可用的数据集大得多，并且基于对语言转换策略的更详细的理解。其次，该数据集用于开发将代码转换纳入当代深度学习语言生成的新方法，包括对话系统、问答、辅助技术、摘要和机器翻译。这一创新将使大量使用多种语言的计算机用户受益，包括那些目前需要用自己说得不流利的语言与机器交互的不那么有特权的用户。该研究项目的成功完成将为开发更适合此类用户的自然语言技术铺平道路，在数字鸿沟上架起桥梁。该项目的总体目标是开发多语言和情境化语言生成技术，这些技术更容易控制，更适合多语言用户。该项目通过完成以下目标来实现这一目标。(1)它发展了基于心理语言学的、可扩展的方法来收集语料库，用于研究多语种使用者在文本对话中如何适应彼此的语言选择。这些方法被用来收集多语言人机对话的大规模、丰富的数据集。这些数据集，以及人类代码转换交互的额外语料库，应该为跨语言使用模式的理论理解提供新的视角，从而更好地理解人们如何在书面语言中使用代码转换。(2)它使用通过这种努力获得的语言学见解来定义预测代码转换的分类器。(3)开发了新的方法来实现高效、大词汇量的神经语言生成，其中包含了这些分类器，允许生成系统以一种对多语言用户来说听起来很自然的方式引入代码转换。因此，这个项目将极大地促进我们对代码转换的理解，特别是在相对未开发的书面对话领域。此外，它的贡献有利于依赖语言生成的广泛应用，包括对话系统、问答、辅助技术、摘要和机器翻译。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Human language technology has recently matured to the extent that computational systems can generally interact with users in ways that are natural to humans, not just to machines. However, most people in the world today are multilingual, and current approaches to language technology do not reflect the reality that multilingual communication is ubiquitous; that is, current technology can interact naturally with monolingual speakers, but not with multilingual ones. Computational systems should be able to generate language that sounds equally natural to these users, and this includes being able to accommodate nonnative speakers. This project first creates a large-scale, broad coverage dataset, reflecting conversations between humans and an automatic system that is sophisticated enough to generate fluent multilingual (i.e. 'code-switched') utterances, but is simple enough for controlled experiments. The dataset is far larger than ones that are currently available, and is based on a much more detailed understanding of language-switching strategies. Second, this dataset is used to develop new methods to incorporate code-switching into contemporary deep-learning language generation, including dialogue systems, question answering, assistive technologies, summarization and machine translation. This innovation should benefit a dramatic number of multilingual computer users, including less privileged users who are currently required to interact with machines in a language they do not speak fluently. Successful completion of the research program will pave the way for the development of natural language technologies that are more accommodating to such users, building bridges over the digital divide. The overarching goal of this project is to develop multilingual and contextualized language generation technologies that are more controllable and more adaptable to multilingual users. The project achieves this goal by completing the following objectives. (1) It develops psycholinguistically-grounded, scalable approaches to collecting corpora for studying how multilingual speakers adapt to each other's linguistic choices in text conversations. These methodologies are employed to collect large-scale, rich datasets of multilingual human-machine conversations. These datasets, as well as additional corpora of human code-switched interactions, should shed new light on the theoretical understanding of cross-lingual usage patterns, allowing for better understanding of how people employ code-switching in written language. (2) It uses the linguistic insights obtained through this endeavor to define classifiers that predict code-switching. (3) Novel approaches are developed for efficient, large-vocabulary neural language generation that incorporate these classifiers, allowing generation systems to introduce code-switching in a way that sounds natural to multilingual users. Consequently, this project should dramatically advance our understanding of code-switching, especially in the relatively unexplored territory of written dialogue. In addition, its contributions benefit a broad range of applications that rely on language generation, including dialogue systems, question answering, assistive technologies, summarization and machine translation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.