NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic

Autor:	Seelawi, Haitham, Mustafa, Ahmad, Al-Bataineh, Hesham, Farhan, Wael, Al-Natsheh, Hussein T.
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared task (Semantic Question Similarity in Arabic). The task was organized as part of the first workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) The goal of the task is to predict whether two questions are semantically similar or not, even if they are phrased differently. A total of 9 teams participated in the task. The datasets created for this task are made publicly available to support further research on Arabic Q2Q. Comment: 8 pages, 2 figure, 3 tables, conference paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.09691 Zobrazit plný text záznamu View this record from Arxiv