快速多任务学习查询拼写纠正

Xuan Sun, Anshumali Shrivastava, Ping Li
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引用次数: 7

摘要

在本文中,我们探索了使用一个新的在线多任务学习框架来完成搜索查询拼写更正任务。在我们的程序中,校正候选项最初由基于排名的系统生成,然后由我们的多任务学习算法重新排名。通过提出的多任务学习方法,我们能够有效地从不同的高偏差训练数据集中传递信息,从而提高所有数据集的拼写正确率。我们在三个查询拼写校正数据集上进行了实验,其中包括著名的TREC基准数据集。实验结果表明,我们提出的方法在精度方面大大优于现有的基线系统。重要的是,所提出的方法在训练速度方面比基线系统快一个数量级。与常用的在线学习方法相比,通常需要超过(例如)60次训练通过,我们提出的方法能够在大约5次通过中接近经验最优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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