改进Quora问题对数据集的问题相似度任务

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI:10.1109/RIVF51545.2021.9642071

H. T. Le, Dung T. Cao, Trung Bui, Long T. Luong, Huy-Quang Nguyen

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引用次数: 2

摘要

语义等价问题的自动检测是问答系统中最重要的一项任务。在由Kaggle组织的Quora问题配对竞赛中发布的Quora数据集，现在已经被许多研究用来训练系统解决识别重复问题的任务。然而，这个数据集上的真实值标签不是100%准确的，可能包括不正确的标签。在本文中，我们专注于通过结合几种策略来提高Quora数据集的质量，这些策略基于Bert、规则和人类重新分配标签。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improve Quora Question Pair Dataset for Question Similarity Task

Automatic detection of semantically equivalent questions is a task of the utmost importance in a question answering system. The Quora dataset, which was released in the Quora Question Pairs competition organized by Kaggle, has now been used by many researches to train the system in solving the task of identifying duplicate questions. However, the ground truth labels on this dataset are not 100% accurate and may include incorrect labeling. In this paper, we concentrate on improving the quality of the Quora dataset by combining several strategies, basing on Bert, rules, and reassigning labels by humans.

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2021 RIVF International Conference on Computing and Communication Technologies (RIVF)

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