  Simultaneous machine translation (SiMT) is usually done via sequence-level
knowledge distillation (Seq-KD) from a full-sentence neural machine translation
(NMT) model. However, there is still a significant performance gap between NMT
and SiMT. In this work, we propose to leverage monolingual data to improve
SiMT, which trains a SiMT student on the combination of bilingual data and
external monolingual data distilled by Seq-KD. Preliminary experiments on En-Zh
and En-Ja news domain corpora demonstrate that monolingual data can
significantly improve translation quality (e.g., +3.15 BLEU on En-Zh). Inspired
by the behavior of human simultaneous interpreters, we propose a novel
monolingual sampling strategy for SiMT, considering both chunk length and
monotonicity. Experimental results show that our sampling strategy consistently
outperforms the random sampling strategy (and other conventional typical NMT
monolingual sampling strategies) by avoiding the key problem of SiMT --
hallucination, and has better scalability. We achieve +0.72 BLEU improvements
on average against random sampling on En-Zh and En-Ja. Data and codes can be
found at https://github.com/hexuandeng/Mono4SiMT.
