Recent years have witnessed a high interest in non-factoid question answering using Community Question Answering (CQA) web sites. Despite ongoing research using state-of-the-art methods, there is a scarcity of available datasets for this task. Why-questions, which play an important role in open-domain and domain-specific applications, are difficult to answer automatically since the answers need to be constructed based on different information extracted from multiple knowledge sources. We introduce the PhotoshopQuiA dataset, a new publicly available set of 2,854 why-question and answer(s) (WhyQ, A) pairs related to Adobe Photoshop usage collected from five CQA web sites. We chose Adobe Photoshop because it is a popular and well-known product, with a lively, knowledgeable and sizeable community. To the best of our knowledge, this is the first English dataset for Why-QA that focuses on a product, as opposed to previous open-domain datasets. The corpus is stored in JSON format and contains detailed data about questions and questioners as well as answers and answerers. The dataset can be used to build Why-QA systems, to evaluate current approaches for answering why-questions, and to develop new models for future QA systems research.
@InProceedings{DULCEANU18.758, author = {Andrei Dulceanu and Thang Le Dinh and Walter Chang and Trung Bui and Doo Soon Kim and Manh Chien Vu and Seokhwan Kim}, title = "{PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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