Ash Lewis and Lingbo Mo will present an update on their work, beginning with a paper called Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction. Since they last presented, they have conducted further experiments and begun planning for a “real user” study. They will also share their thoughts on potential future work for feedback. An abstract of the paper can be found below.
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction
Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) in one turn. However, because natural language may contain ambiguity and variability, this is a difficult challenge. In this work, we investigate an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, aiming to increase the transparency of the parsing process and help the user trust the final answer. We construct INSPIRED, a crowdsourced dialogue dataset derived from the ComplexWebQuestions dataset. Our experiments show that this framework has the potential to greatly improve overall parse accuracy. Furthermore, we develop a pipeline for dialogue simulation to evaluate our framework w.r.t. a variety of state-of-the-art KBQA models without further crowdsourcing effort. The results demonstrate that our frameworkpromise s to be effective across such models.