DeText: A Deep Text Ranking Framework with BERT
CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virtual Event Ireland October, 2020(2020)
摘要
Ranking is the most important component in a search system. Most search systems deal with large amounts of natural language data, hence an effective ranking system requires a deep understanding of text semantics. Recently, deep learning based natural language processing (deep NLP) models have generated promising results on ranking systems. BERT is one of the most successful models that learn contextual embedding, which has been applied to capture complex query-document relations for search ranking. However, this is generally done by exhaustively interacting each query word with each document word, which is inefficient for online serving in search product systems. In this paper, we investigate how to build an efficient BERT-based ranking model for industry use cases. The solution is further extended to a general ranking framework, DeText, that is open sourced and can be applied to various ranking productions. Offline and online experiments of DeText on three real-world search systems present significant improvement over state-of-the-art approaches.
更多查看译文
关键词
Ranking,Deep Language Models,Natural Language Processing
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要