Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain
  Adaptation in Neural Machine Translation
Keyu Chen Di Zhuang Mingchen Li J. Morris Chang
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.