Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy

Autor:	Gupta, Deepak, Pujari, Rajkumar, Ekbal, Asif, Bhattacharyya, Pushpak, Maitra, Anutosh, Jain, Tom, Sengupta, Shubhashis
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this paper, we propose a hybrid technique for semantic question matching. It uses our proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning (DL) or taxonomy-based knowledge alone. Comment: Paper was accepted at COLING 2018, presented as a poster
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.08201 Zobrazit plný text záznamu View this record from Arxiv