Towards Transparent Interactive Semantic Parsing via Step-by-Step
Correction
- URL: http://arxiv.org/abs/2110.08345v1
- Date: Fri, 15 Oct 2021 20:11:22 GMT
- Title: Towards Transparent Interactive Semantic Parsing via Step-by-Step
Correction
- Authors: Lingbo Mo, Ashley Lewis, Huan Sun, Michael White
- Abstract summary: We investigate an interactive semantic parsing framework that explains the predicted logical form step by step in natural language.
We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework.
Our experiments show that the interactive framework with human feedback has the potential to greatly improve overall parse accuracy.
- Score: 17.000283696243564
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existing studies on semantic parsing focus primarily on mapping a
natural-language utterance to a corresponding logical form in one turn.
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 that explains the predicted logical form
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
appropriately trust the final answer. To do so, we construct INSPIRED, a
crowdsourced dialogue dataset derived from the ComplexWebQuestions dataset. Our
experiments show that the interactive framework with human feedback 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 involving further crowdsourcing effort.
The results demonstrate that our interactive semantic parsing framework
promises to be effective across such models.
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