Multi-Task Neural Model for Agglutinative Language Translation
Yirong Pan, Xiao Li, Yating Yang, Rui Dong
Student Research Workshop SRW Paper
Session 2B: Jul 6
(09:00-10:00 GMT)
Session 11B: Jul 8
(06:00-07:00 GMT)
Abstract:
Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora. However, it struggles in the low-resource and morphologically-rich scenarios of agglutinative language translation task. Inspired by the finding that monolingual data can greatly improve the NMT performance, we propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming. Our approach employs the shared encoder and decoder to train a single model without changing the standard NMT architecture but instead adding a token before each source-side sentence to specify the desired target outputs of the two different tasks. Experimental results on Turkish-English and Uyghur-Chinese show that our proposed approach can significantly improve the translation performance on agglutinative languages by using a small amount of monolingual data.
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