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The TechQA Dataset

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arxiv 1911.02984 v1 pith:C6GXOMC7 submitted 2019-11-08 cs.CL cs.IR

The TechQA Dataset

Vittorio Castelli , Rishav Chakravarti , Saswati Dana , Anthony Ferritto , Radu Florian , Martin Franz , Dinesh Garg , Dinesh Khandelwal
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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
This is my paper
classification cs.CL cs.IR
keywords domaintechnicaltechqadatasetquestionsactualquestionrather
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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