I will presenting the work I’ve been doing on my QP2 in which I am attempting to use RSA (Rational Speech Act) approaches to improve a question generation model. This work is an extension of previous work on Transparent Interactive Semantic Parsing, in which we develop a dialogue agent that helps a human user query a knowledge base in natural language. The dialogue agent parses an NL utterance into a SPARQL query, decomposes it into pieces, retrieves answers, then translates the entire process into a series of natural language sub-questions so that the user can validate the results or make corrections as necessary. The current work focuses on the sub-question generation sub-task, in which it is very important for the question to accurately and coherently represent the meaning of its SPARQL query. To this end, I experiment with RSA-style approaches of explicit modeling of a listener to improve the generator. Specifically in this work I focus on a “reconstructor”-based method in which a listener model is trained to recover the original meaning representation (SPARQL query) from a base speaker model. I will show my experiments with self-training using the reconstructor-based model and detail my in-progress work with a “distractor”-based approach, in which the model attempts to generate an utterance that distinguishes an input from possible distractor inputs.