SBIR Phase I: A System for Automating End-to-End Creation of Natural Language Interfaces

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是通过对话界面更有效地访问组织和企业的信息，商品和服务。到2017年底，美国估计安装了3300万台语音激活设备，消费者开始看到这些设备上商业服务的增长带来的好处。以自然语言与计算机进行交互比以往任何时候都更有效，企业迫切希望将其服务纳入这种新模式。然而，虽然机器学习的进步提高了构建会话界面的速度和准确性，但今天的解决方案仍然会导致开发过程中的主要瓶颈。解决这些瓶颈将为企业节省大量时间和资源，并为最终用户提供更多便利。这个小型企业创新研究（SBIR）第一阶段项目推进机器学习和自然语言处理技术的发展，从而能够快速创建会话界面，并提高个人与计算机交互的效率。尽管最近在会话界面工具方面取得了重大进展，但当前构建健壮会话界面的瓶颈仍然存在，因为开发过程的一部分从高度自动化转向手动编程。相反，该方法将使用创新的LSTM序列到序列机器学习模型来学习和模拟网页上的人类行为，以及用于开放域问题回答的新系统，实现端到端自动化。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable more efficient access to information, goods, and services from organizations and businesses through conversational interfaces. With an estimated 33 million voice-activated devices installed in the U.S. by the end of 2017, consumers are beginning to see the benefits of the rise in business services on these devices. Interacting with computers in natural language is more efficient than ever before, and business are urgently seeking to include their services on this new modality. Yet, while advances in machine learning have increased the speed and accuracy with which conversational interfaces are built, today's solutions still result in major bottlenecks in the development process. Addressing these bottlenecks would result in significant savings of time and resources for businesses, in addition to increased convenience for end users.This Small Business Innovation Research (SBIR) Phase I project advances the development of machine learning and natural language processing technologies that will enable rapid creation of conversational interfaces and increase the efficiency with which individuals interact with computers. Despite major recent advances in conversational interface tooling, current bottlenecks in building robust conversational interfaces still exist, as portions of the development process go from highly-automated to manually-programmed. Instead, the proposed approach will achieve end-to-end automation using an innovative LSTM sequence-to-sequence machine learning model to learn and simulate human behavior on a web page, as well as a new system for open-domain question answering.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.