基于多任务学习的跨语言信息检索方法研究

doi:10.16088/j.issn.1001-6600.2022022201

Abstract

Abstract: Cross-language information retrieval is one of the important tasks in the field of information retrieval. Existing cross-lingual neural retrieval methods usually use single-task learning, and the single feature capture model limits the performance of neural retrieval models. Therefore, a cross-language retrieval method based on multi-task learning is proposed, which uses a text classification task as a secondary task and captures feature information of both tasks simultaneously using a shared text feature extraction layer so that it learns the feature patterns of different tasks, and then feeds the feature vectors into the neural retrieval model and the text classification model to complete the two tasks, respectively. In addition, the external corpus introduced by the text classification task also plays a role in data augmentation to a certain extent, further increasing the level of feature information. Experiments conducted on four language pairs from the CLEF 2000-2003 dataset show that the present method significantly improves the text feature extraction and thus enhances the neural retrieval model performance, increasing the MAP values of the neural retrieval model by 0.012-0.188 and increased the speed of model convergence by an average of 24.3%.

Key words: information retrieval, multi-task learning, cross-language information retrieval, neural retrieval model, external corpus

CLC Number:

TP391.3

DAI Jiayang, ZHOU Dong. Research on Cross-Language Information Retrieval Method Based on Multi-task Learning[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(6): 69-81.

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