Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
Priyanka Sen, Armin Oliya, Amir Saffari
Abstract
End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al, 2020) have focused on single-entity questions using a relation following operation. In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. We find that introducing intersection improves performance over a baseline model on two datasets, WebQuestionsSP (69.6% to 73.3% Hits@1) and ComplexWebQuestions (39.8% to 48.7% Hits@1), and in particular, improves performance on questions with multiple entities by over 14% on WebQuestionsSP and by 19% on ComplexWebQuestions.
BibTeX
@inproceedings{sen-etal-2021-expanding,
title = "Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection",
author = "Sen, Priyanka and
Oliya, Armin and
Saffari, Amir",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.694/",
doi = "10.18653/v1/2021.emnlp-main.694",
pages = "8805--8812"
}