Fast multi-task learning for query spelling correction

In this paper, we explore the use of a novel online multi-task learning framework for the task of search query spelling correction. In our procedure, correction candidates are initially generated by a ranker-based system and then re-ranked by our multi-task learning algorithm. With the proposed multi-task learning method, we are able to effectively transfer information from different and highly biased training datasets, for improving spelling correction on all datasets. Our experiments are conducted on three query spelling correction datasets including the well-known TREC benchmark dataset. The experimental results demonstrate that our proposed method considerably outperforms the existing baseline systems in terms of accuracy. Importantly, the proposed method is about one order of magnitude faster than baseline systems in terms of training speed. Compared to the commonly used online learning methods which typically require more than (e.g.,) 60 training passes, our proposed method is able to closely reach the empirical optimum in about 5 passes.