Year: 2020
Type: conference-paper
Abstract: A neural machine translation (NMT) system is expensive to train, especially with highresource settings.As the NMT architectures become deeper and wider, this issue gets worse and worse.In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method.We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence.The normbased sentence difficulty takes the advantages of both linguistically motivated and modelbased sentence difficulties.It is easy to determine and contains learning-dependent features.The norm-based model competence makes NMT learn the curriculum in a fully automated way, while the norm-based sentence weight further enhances the learning of the vector representation of the NMT.Experimental results for the WMT'14 English-German and WMT'17 Chinese-English translation tasks demonstrate that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56)and training speedup (2.22x/3.33x).
Authors: Xuebo Liu, Houtim Lai, Derek F. Wong, Lidia S. Chao
Institution: University of Macau
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W3034938700