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Inability of the naive users to formulate appropriate queries is afundamental problem in web search engines. Therefore, assisting users to issuemore effective queries is an important way to improve users' happiness. Oneeffective approach is query reformulation, which generates new effectivequeries according to the current query issued by users. Previous researchestypically generate words and phrases related to the original query. Since thedefinition of query reformulation is quite general, it is completely difficultto develop a uniform term-based approach for this problem. This paper usesreadily available data, particularly over one billion anchor phrases inClueweb09 corpus, in order to learn an end-to-end encoder-decoder model toautomatically generate effective queries. Following successful researches inthe field of sequence to sequence models, we employ a character-levelconvolutional neural network with max-pooling at encoder and an attention-basedrecurrent neural network at decoder. The whole model learned in an unsupervisedend-to-end manner.Experiments on TREC collections show that the reformulatedqueries automatically generated by the proposed solution can significantlyimprove the retrieval performance.

Learning to Reformulate the Queries on the WEB