Natural Language Interfaces like Siri and Alexa help people do things more efficiently, but they are brittle, unable to handle the full range of ways people naturally express themselves. Each of their actions is manually defined by developers, with limited ability to compose actions to make more sophisticated ones. The choice of action is made by a statistical model that is limited by the range of data seen in training. Despite steady progress in the accuracy of these systems, the true scope of remaining challenges has been obscured by the way researchers collect and prepare data.

In this talk, I will describe two of my projects that have revealed previously unknown limitations of natural language interfaces and ways to address them. First, I will show that systems for converting questions to SQL queries have limited generalizability beyond examples seen in training (ACL 2018). I propose a new model and a new way to split data into training and test sets that explore this challenge. Second, I will show that standard crowd-worker data collection processes miss the long and heavy tail of ways people speak (ACL 2017). I propose an outlier-based data collection workflow (NAACL 2019), and a complementary taboo list workflow (EMNLP 2020), that improve data diversity and reduce the cost of data cleaning. I will conclude by outlining a research agenda for fundamentally changing the capabilities of these systems. Today we use these systems to do simple tasks, e.g. “start a 5 minute timer”. My work will enable systems to do complex tasks as part of applications, e.g. “Plot population over the last 2000 years with a trend line only and a log scale on the y-axis”.