{"type":"ScholarlyArticle","title":"Quality Matters: Assessing cQA Pair Quality via Transductive Multi-View Learning","authors":[{"name":"Xiaochi Wei","slug":"xiaochi-wei","given":"Xiaochi","family":"Wei","affiliation":"Beijing ER Center of HLIPCA , School of Computer , Beijing Institute of Technology"},{"name":"Heyan Huang","slug":"heyan-huang","given":"Heyan","family":"Huang","affiliation":"Beijing ER Center of HLIPCA , School of Computer , Beijing Institute of Technology"},{"name":"Liqiang Nie","slug":"liqiang-nie","given":"Liqiang","family":"Nie","affiliation":"School of Computer Science , Shandong University"},{"name":"Fuli Feng","slug":"fuli-feng","given":"Fuli","family":"Feng","affiliation":"School of Computing , National University of Singapore"},{"name":"Richang Hong","slug":"richang-hong","given":"Richang","family":"Hong","affiliation":"School of Computer and Information , Hefei University of Technology"},{"name":"Tat-Seng Chua","slug":"tat-seng-chua","given":"Tat-Seng","family":"Chua","affiliation":"School of Computing , National University of Singapore"}],"abstract":"Community-based question answering (cQA) sites have become important knowledge sharing platforms, as massive cQA pairs are archived, but the uneven quality of cQA pairs leaves information seekers unsatisfied. Various efforts have been dedicated to predicting the quality of cQA contents. Most of them concatenate different features into single vectors and then feed them into regression models. In fact, the quality of cQA pairs is influenced by different views, and the agreement among them is essential for quality assessment. Besides, the lacking of labeled data significantly hinders the quality prediction performance. Toward this end, we present a transductive multi-view learning model. It is designed to find a latent common space by unifying and preserving information from various views, including question, answer, QA relevance, asker, and answerer. Additionally, rich information in the unlabeled test cQA pairs are utilized via transductive learning to enhance the representation ability of the common space. Extensive experiments on real-world datasets have well-validated the proposed model.","doi":"10.24963/ijcai.2018/623","url":"https://ijcai.org/proceedings/2018/623","pdf":"https://ijcai.org/proceedings/2018/0623.pdf","markdown":"https://ijcai.org/proceedings/2018/623.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/623","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":623,"pages":{"first":4482,"last":4488},"keywords":["Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Multi-instance;Multi-label;Multi-view learning","Natural Language Processing: Question Answering"],"videos":[],"references":[{"position":1,"raw":"[Blum and Mitchell, 2000] Avrim Blum and Tom Mitchell. 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