I am a research scientist in the Language and Translation Technologies team at Facebook AI Applied Research. I develop novel machine learning solutions for natural language understanding problems. I build AI applications that interact with and learn from people.
My recent research has been under the following themes:
Previously I was a senior research scientist at Salesforce Research, where I led Photon and other projects in semantic parsing. I was a graduate student at the Paul G. Allen School of Computer Science & Engineering, University of Washington, working with Luke Zettlemoyer and Michael D. Ernst on data-driven natural language programming.
Photon is a cross-domain natural language interface to databases (X-NLIDB) that handles factual look-up queries.
It allows end users to query a large number of relational DBs in natural language, including DBs it has never been trained on.
The core of the system is a strong neural text-to-SQL semantic parser trained using thousands of NL-SQL pairs grounded to hundreds of DBs.
Photon adopts the principle of better saying no than making a mistake, and verifies the input question is indeed translatable to a structured query language before executing the neural semantic parser, which effectively improves its robustness.
Ask Photon questions about the data and tease out its power.
[EMNLP'20 Findings, ACL'20 System Demonstration]
Tellina is an end-user scripting assistant that can be queried via natural language.
It translates a natural language sentence typed by the user into a piece of short, executable script.
The underlying models are neural encoder-decoders trained on NL-script pairs collected by programming experts from online tutorials and question-answering forums.
We instantiate the prototype in Bash.
This work poses several challenges including scalable data collection, never-ending learning and personalization, most of which are central to all practical semantic parsing systems.
[LREC'18, UW-CSE-TR'17]