Computational Semantics and Evaluation Benchmark for Interrogative Sentences via Combinatory Categorial Grammar
This work addresses the problem of formalizing and evaluating interrogative sentence semantics for computational linguistics, but it is incremental as it builds on existing CCG frameworks and parsers.
The authors developed a compositional semantics for polar and wh-questions using Combinatory Categorial Grammar (CCG) and created a question-answering dataset QSEM to evaluate it, obtaining annotated CCG trees and semantic representations for about half of the QSEM samples.
We present a compositional semantics for various types of polar questions and wh-questions within the framework of Combinatory Categorial Grammar (CCG). To assess the explanatory power of our proposed analysis, we introduce a question-answering dataset QSEM specifically designed to evaluate the semantics of interrogative sentences. We implement our analysis using existing CCG parsers and conduct evaluations using the dataset. Through the evaluation, we have obtained annotated data with CCG trees and semantic representations for about half of the samples included in QSEM. Furthermore, we discuss the discrepancy between the theoretical capacity of CCG and the capabilities of existing CCG parsers.