  Inability of the naive users to formulate appropriate queries is a
fundamental problem in web search engines. Therefore, assisting users to issue
more effective queries is an important way to improve users' happiness. One
effective approach is query reformulation, which generates new effective
queries according to the current query issued by users. Previous researches
typically generate words and phrases related to the original query. Since the
definition of query reformulation is quite general, it is completely difficult
to develop a uniform term-based approach for this problem. This paper uses
readily available data, particularly over one billion anchor phrases in
Clueweb09 corpus, in order to learn an end-to-end encoder-decoder model to
automatically generate effective queries. Following successful researches in
the field of sequence to sequence models, we employ a character-level
convolutional neural network with max-pooling at encoder and an attention-based
recurrent neural network at decoder. The whole model learned in an unsupervised
end-to-end manner.Experiments on TREC collections show that the reformulated
queries automatically generated by the proposed solution can significantly
improve the retrieval performance.
