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了这样的程度，计算系统通常可以以人类自然的方式与用户互动，而不仅仅是机器。然而，当今世界上的大多数人都是多语种的，目前的语言技术方法并没有反映出多语种交流无处不在的现实；也就是说，目前的技术可以与单语者自然互动，但不能与多语者互动。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。该项目首先创建了一个大规模、覆盖面广的数据集，反映了人类和一个自动系统之间的对话，该系统足够复杂，可以生成流利的多语言(即“代码转换”)话语，但对于受控实验来说，它足够简单。该数据集比目前可用的数据集要大得多，并且基于对语言转换策略的更详细的理解。其次，利用这一数据集开发了将语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。这一创新将使数量可观的多语种计算机用户受益，包括目前被要求以一种不流利的语言与机器交互的较低特权用户。研究计划的成功完成将为开发更适合这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更可控、更适合多语种用户的多语种和语境化语言生成技术。该项目通过完成以下目标实现了这一目标。(1)它开发了基于心理语言学的、可扩展的方法来收集语料库，以研究多语种说话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大规模、丰富的多语言人机对话数据集。这些数据集，以及人类语码转换互动的额外语料库，应该有助于从理论上理解跨语言使用模式，从而更好地理解人们如何在书面语言中使用语码转换。(2)它使用通过这一努力获得的语言学洞察力来定义预测语码转换的量词。(3)开发了用于高效、大词汇量的神经语言生成的新方法，该方法结合了这些分类器，允许生成系统以对多语言用户听起来自然的方式引入代码切换。因此，这个项目应该会极大地促进我们对代码转换的理解，特别是在相对未被探索的书面对话领域。此外，它的贡献使依赖于语言生成的广泛应用程序受益，包括对话系统、问题回答、辅助技术、摘要和机器翻译。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.