Semantic Parsing To Probabilistic Programs For Situated Question Answering
Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem is challenging because it requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs, a novel situated question answering model that can use background knowledge and global features of the question/environment interpretation while retaining efficient approximate inference. Our key insight is to treat semantic parses as probabilistic programs that execute nondeterministically and whose possible executions represent environmental uncertainty. We evaluate our approach on a new, publicly-released data set of 5000 science diagram questions, outperforming several competitive classical and neural baselines.
If time permits, I will also talk about some more recent work combining probabilistic programming and neural networks for program induction.
Jayant Krishnamurthy is a research scientist at the Allen Institute for Artificial Intelligence. He received his Ph.D. in Computer Science from Carnegie Mellon University in 2015. His research interests are natural language understanding for tasks such as question answering.