Attention-based skill translation models for expert finding
作者：
Highlights：
• Proposing two attention-based neural networks for the task of skill translation.
• Using skill translations to fill the semantic gap between skill and candidates.
• Utilizing context-aware information to find semantically related translations for skills.
• Achieving the best performance even with a few translations.
• Usability of the proposed networks as multi-label document classifiers.
摘要
•Proposing two attention-based neural networks for the task of skill translation.•Using skill translations to fill the semantic gap between skill and candidates.•Utilizing context-aware information to find semantically related translations for skills.•Achieving the best performance even with a few translations.•Usability of the proposed networks as multi-label document classifiers.
论文关键词：Expert finding,Semantic matching,Translation models,StackOverflow
论文评审过程：Received 11 February 2021, Revised 20 November 2021, Accepted 19 December 2021, Available online 11 January 2022, Version of Record 19 January 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116433
