I work on dialogue systems and Natural Language Generation (NLG). Currently I am working on an interactive semantic parsing system, the goal of which is to make more accessible the information stored in databases and knowledge bases (KBs) for novice users. Because this information is often accessed using query languages such as SQL and SPARQL, one must be well-versed in these languages in order to query the DB or KB. Automatic semantic parsers aim to act as translators between natural language and a query language, however current state-of-the-art systems still have far-from-perfect accuracy. Thus, interactive semantic parsing systems take a human-in-the-loop approach, allowing users to make corrections to incorrect parses. Our system conducts this process in the form of a dialogue, in which the AI agent takes a natural-language query from the user, produces a parse (logical form), then decomposes that parse into pieces, translate them to natural language in a sequence of sub-questions, and shows these to the use, who can give corrections. This work, which I presented at ACL 2022 in Dublin, can be found here. A follow-up work, presented by my co-author at InterNLP, can be found here.

In terms of NLG, I have been focused on improving the question generation model in the above system, incorporating Rational Speech Act (RSA) methods to improve the accuracy and clarity of generated questions. I model both a listener and a speaker, which are iteratively reasoning about one another's behavior to select the best utterance. At this stage, it appears that this approach is effective at reducing hallucination (where the model outputs "extra" information that does not appear in the input) and omissions (where the model output fails to mention key information).

I was also part of a team that participated in the AlexaPrize Taskbot Challenge, in which we achieved 3rd place.

I also presented at YRRSDS in Edinburgh, Scotland. Proceedings can be found here.

My CV can be found here.