Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval
| Autor: | Zhenchang Xing, Xin Xia, Qiao Huang, David Lo, Neng Zhang, Ying Zou |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Software Engineering. 48:1185-1211 |
| ISSN: | 2326-3881 0098-5589 |
| Popis: | Technical QA (2)Chatbot4QR can rapidly respond to the participants after receiving a query within ~1.3 seconds; (3)The refined queries contribute to retrieving more relevant SO questions than nine baseline approaches. For more than 70% of the participants who have preferred techniques on the query tasks, Chatbot4QR significantly outperforms the state-of-the-art word embedding-based retrieval approach with an improvement of at least 54.6% in terms of Pre@k and NDCG@k; and (4)For 48%-88% of the assigned query tasks, the participants obtain more desired results after interacting with Chatbot4QR than directly searching from Web search engines (e.g., the SO search engine and Google) using the original queries. |
| Databáze: | OpenAIRE |
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