12,129 research outputs found
A Multi-Task Architecture on Relevance-based Neural Query Translation
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.Comment: Accepted for publication at ACL 201
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