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arXiv · 1906.06849

A Multi-Task Architecture on Relevance-based Neural Query Translation

Sheikh Muhammad Sarwar·Hamed Bonab·James Allan
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Abstract

We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translation process for Cross-lingual Information Retrieval (CLIR) task is usually treated as a black box and it is performed as an independent step. However, an NMT model trained on sentence-level parallel data is not aware of the vocabulary distribution of the retrieval corpus. We address this problem with our multi-task learning architecture that achieves 16% improvement over a strong NMT baseline on Italian-English query-document dataset. We show using both quantitative and qualitative analysis that our model generates balanced and precise translations with the regularization effect it achieves from multi-task learning paradigm.

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BibTeXRIS

Sheikh Muhammad Sarwar, Hamed Bonab, James Allan. 2019-06-17. A Multi-Task Architecture on Relevance-based Neural Query Translation. https://arxiv.org/abs/1906.06849

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