Abstract
We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We adapt sequentially across two Spanish-English and three English-German tasks, comparing unregularized fine-tuning, L2 and Elastic Weight Consolidation. We then report a novel scheme for adaptive NMT ensemble decoding by extending Bayesian Interpolation with source information, and show strong improvements across test domains without access to the domain label.
Abstract (translated)
我们研究自适应集成加权神经机器翻译，解决的情况下，提高性能在一个新的和潜在的未知领域，而不牺牲性能在原来的领域。我们依次适应了两个西班牙语-英语和三个英语-德语任务，比较了非规范微调、L2和弹性重量巩固。然后，我们提出了一种新的基于源信息扩展贝叶斯插值的自适应NMT集成译码方案，并在不访问域标签的情况下，对测试域进行了改进。
URL
https://arxiv.org/abs/1906.00408
