Neural Machine Translation (NMT) models have become successful, but their
performance remains poor when translating on new domains with a limited number
of data. In this paper, we present a novel approach Epi-Curriculum to address
low-resource domain adaptation (DA), which contains a new episodic training
framework along with denoised curriculum learning. Our episodic training
framework enhances the model's robustness to domain shift by episodically
exposing the encoder/decoder to an inexperienced decoder/encoder. The denoised
curriculum learning filters the noised data and further improves the model's
adaptability by gradually guiding the learning process from easy to more
difficult tasks. Experiments on English-German and English-Romanian translation
show that: (i) Epi-Curriculum improves both model's robustness and adaptability
in seen and unseen domains; (ii) Our episodic training framework enhances the
encoder and decoder's robustness to domain shift.

神经机器翻译模型在新领域上的性能仍然较差，本研究提出了一种名为 Epi-Curriculum 的新方法来解决低资源域适应问题，该方法结合了新的经验教学框架和去噪课程学习，通过使编码器 / 解码器与经验不足的解码器 / 编码器进行经验性训练，增强了模型对领域转移的鲁棒性，去噪课程学习则通过逐渐引导学习过程从简单到更困难的任务，进一步提高了模型的适应性。实验结果表明 Epi-Curriculum 提高了模型在可见和不可见领域的鲁棒性和适应性，我们的经验性训练框架增强了编码器和解码器对领域转移的鲁棒性。