Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.
REF proposed a lattice search algorithm for NMT adaptation, however, this algorithm assumes access to lattices generated from a phrase based machine translation system.
Neural Lattice Search for Domain Adaptation in Machine Translation
IJCNLP
Computer Science