Using re-ranking to boost deep learning based community question retrieval

K. Ghosh, Plaban Kumar Bhowmick, Pawan Goyal
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引用次数: 6

Abstract

The current study presents a two-stage question retrieval approach which, in the first phase, retrieves similar questions for a given query using a deep learning based approach and in the second phase, re-ranks initially retrieved questions on the basis of inter-question similarities. The suggested deep learning based approach is trained using several surface features of texts and the associated weights are pre-trained using a deep generative model for better initialization. The proposed retrieval model outperforms standard baseline question retrieval approaches. The proposed re-ranking approach performs inference over a similarity graph constructed with the initially retrieved questions and re-ranks the questions based on their similarity with other relevant questions. Suggested re-ranking approach significantly improves the precision for the retrieval task.
利用重新排序促进深度学习社区问题检索
目前的研究提出了一种两阶段的问题检索方法,在第一阶段,使用基于深度学习的方法为给定查询检索相似的问题,在第二阶段,根据问题之间的相似性对最初检索到的问题重新排序。建议的基于深度学习的方法使用文本的几个表面特征进行训练,并使用深度生成模型对相关权重进行预训练,以便更好地初始化。提出的检索模型优于标准基线问题检索方法。提出的重新排序方法对由最初检索到的问题构造的相似图进行推理,并根据问题与其他相关问题的相似度对问题进行重新排序。提出的重新排序方法显著提高了检索任务的精度。
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