深度学习检索框架的前沿探索

Exploration on Neural Information Retrieval Framework

经过几十年的发展,信息检索技术获得了长足的进步和广泛的应用,但当前主流的搜索引擎系统距离真正智能的信息获取系统仍然有较大差距.智能信息获取系统能够对网络大数据的内容进行获取、阅读和理解,对关键语义信息实现存储和检索,并能够依据用户的信息需求进行推理、决策和信息生成.实现这样的系统,迫切需要在检索架构和检索模型上形成根本性的改变和理论突破.近年来,围绕智能信息获取的需求,利用深度学习检索框架展开了系统性研究,在数据表征、数据索引以及检索算法等方向上形成了一系列原创成果,在探索全新的深度学习检索架构上不断迈进.

After decades of research, information retrieval technology has been significantly advanced and widely applied in our daily life. However, there is still a huge gap between modern search engines and true intelligent information accessing systems. In our opinion, an intelligent information accessing system should be able to crawl, read and understand the content of the big Web data, index and search the key semantic information, and reason, decide and generate the right results based on users＇ information need. To develop such kind of systems, we need theoretical breakthrough on the search architecture and models. In recent years, to address the intelligent information accessing problem, we have conducted systematical research on neural information retrieval framework. We have achieved a few of original contributions on text representation, data indexing and relevance matching. However, there is still a long way in this direction and we will continue our exploration on neural information retrieval in the future.