Search engine has become a fundamental component in various web and mobile applications. Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries. In this paper, we explore a vector space search framework for document retrieval. Specifically, we trained a deep semantic matching model so that each query and document can be encoded as a low dimensional embedding. Our model was trained based on BERT architecture. We deployed a fast k-nearest-neighbor index service for online serving. Both offline and online metrics demonstrate that our method improved retrieval performance and search quality considerably, particularly for tail queries Search engine has been widely applied in plenty of areas on the internet, which receives a query provided by users and returns a list of relevant documents within sub-seconds, helping users obtain their desired information instantaneously. Numerous technologies have been developed and utilized in real-world search engine systems . However, the existing semantic gap between search queries and documents, makes it challenging to retrieve the most relevant documents from tens of millions of documents. Therefore, there is still a large proportion of search requests that can not be satisfied perfectly, especially for long tail queries.A search engine system is usually composed of three main modules, -query understanding module -retrieval module -ranking module The query understanding module first parses the original query string into a structured query object BID32 . More specifically, the query understanding module includes several subtasks, such as word segmentation, query correction, term importance analyze, query expansion, and query rewrite, etc. After the query string was parsed, an index module accepts the parsed query, and then retrieve the candidate documents.We call this stage the retrieval stage or the first round stage. Most web-scale search engine systems use the term inverted index for document retrieval, where term is the most basic unit in the whole retrieval procedure. In the first round stage, the retrieved documents are ranked by a simple relevance model, eg TF-IDF, BM25, and the top-N documents with the highest score are submitted to the next stage for ranking. Finally, the documents scored largest by a ranking function are returned to users eventually. For a search system described above, the final retrieval performance is highly enslaved by these query understanding module. Take word segmentation as an example: this task segments raw continuous query string into a list of segmented terms. Since the word segmentation algorithm has the risk of wrong segmentation. If the error segmented term does not appear in the document space, then no document could be retrieved in the first round stage, and it will return a result page without any document which damages the user's experience seriously.There is a lot of work focused on better understanding queries to retrieve more relevant documents. However, since the final performance is influenced by all parts of the query understanding module. Attempts to optimize only one part is usually hard to contribute to a significant enhancement. To avoid the problems mentioned above, we propose a novel complementary retrieval sys-tem that retrieves documents without the traditional term-based retrieval framework. That is, instead of parse raw query into a structured query, we directly map both queries and documents into a low dimension of embedding. Then in the online serving, the k-nearest-neighbor documents of the given query in the latent embedding space are searched for retrieval.Recently, we have witnessed tremendous successful applications of deep learning techniques in information retrieval circle, like query document relevance matching BID14 BID34 BID33 , query rewriting BID13 , and search result ranking BID12 BID10 . However, it is still hard to directly retrieve relevant documents using an end2end fashion based on knearest-neighbor search in latent space, especially for long tail queries.The latest far-reaching advancement in natural language processing with deep learning, BERT BID8 , provides a turning point to make end2end retrieval realizable. In this paper, we present a document retrieval framework as a supplement to the traditional inverted index based retrieval system. We design a new architecture to retrieve documents without a traditional term-based query understanding pipeline, which avoids performance decay by each subtask of query understanding. We use BERT architecture as the general encoder of query and document strings, then we fine-tuned the pre-trained BERT model with human annotated data and negative sampling technique. Finally, we conduct both offline and online experiments to verify our proposed method. To sum up, our main contributions are described below:1. We design a novel end2end document retrieval framework ，which is a supplement to traditional term-based methods.2. Our model is trained on transformer architecture, and a series of training techniques are developed for performance enhancement.3. The proposed techniques can not only be used in document retrieval but also have a significant improvement for search ranking.The rest of the paper is organized as follows. We concisely review the related work in Section 2. Sections 3 mainly describes our proposed methods. Offline and online experiments are detailed given in Section 4 and Section 5 respectively. Finally, we conclude and discuss future work in Section 6. In this paper, we present an architecture for semantic document retrieval. In this architecture, we first train a deep representation model for query and document embedding, then we build our semantic index using a fast k-nearest-neighbor vector search engine. Both offline and online experiments have shown that retrieval performance is greatly enhanced by our method. For the future work, we would like to explore a more general framework that could use more signals involved for semantic retrievals, like document quality features, recency features, and other text encoding models.
A deep semantic framework for textual search engine document retrieval
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