Ash Lewis and Lingbo Mo will present their work with Huan Sun and Mike White titled “Transparent Dialogue for Step-by-Step Semantic Parse Correction”. Here’s the abstract:
Existing studies on semantic parsing focus primarily on mapping a natural-language utterance to a corresponding logical form in a one-shot setting. However, because natural language can contain a great deal of ambiguity and variability, this is a difficult challenge. In this work, we investigate an interactive semantic parsing framework, which shows the user how a complex question is answered step-by-step and enables them to make corrections through natural-language feedback to each step in order to increase the clarity and accuracy of parses. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, and construct INSPIRED, a transparent dialogue dataset with complex questions, predicted logical forms, and step-by-step, natural-language feedback. Our experiments show that the interactive framework with human feedback can significantly improve the overall parse accuracy. Furthermore, we develop a pipeline for dialogue simulation to apply the framework to other various state-of-the-art models for KBQA and largely improve their performance as well, which sheds light on the generalizability of this framework for other parsers without further annotation effort.