Paraphrase-focused learning to rank for domain-specific frequently asked questions retrieval
作者：
Highlights：
• We study the potential of supervised learning to rank for FAQ retrieval.
• Supervised models offer performance improvements for this task.
• We explored low-effort paraphrase-based data labeling strategies.
• Paraphrase-based labeling was effective for the best models on two FAQ data collections.
• We make a new FAQ retrieval data set publicly available.
摘要
•We study the potential of supervised learning to rank for FAQ retrieval.•Supervised models offer performance improvements for this task.•We explored low-effort paraphrase-based data labeling strategies.•Paraphrase-based labeling was effective for the best models on two FAQ data collections.•We make a new FAQ retrieval data set publicly available.
论文关键词：Question answering,FAQ retrieval,Learning to rank,ListNET,LambdaMART,Convolutional neural network
论文评审过程：Received 14 June 2017, Revised 11 September 2017, Accepted 12 September 2017, Available online 12 September 2017, Version of Record 5 October 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.031
