The TechQA Dataset

Vittorio Castelli; Rishav Chakravarti; Saswati Dana; Anthony Ferritto; Radu Florian; Martin Franz; Dinesh Garg; Dinesh Khandelwal; Scott McCarley; Mike McCawley; Mohamed Nasr; Lin Pan; Cezar Pendus; John Pitrelli; Saurabh Pujar; Salim Roukos; Andrzej Sakrajda; Avirup Sil; Rosario Uceda-Sosa; Todd Ward; Rong Zhang

arXiv · 1911.02984

The TechQA Dataset

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Abstract

We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size -- 600 training, 310 dev, and 490 evaluation question/answer pairs -- thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TechQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote---a technical document that addresses a specific technical issue. We also release a collection of the 801,998 publicly available Technotes as of April 4, 2019 as a companion resource that might be used for pretraining, to learn representations of the IT domain language.

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Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Mike McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avirup Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang. 2019-11-08. The TechQA Dataset. https://arxiv.org/abs/1911.02984

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