  We study multilingual AMR parsing from the perspective of knowledge
distillation, where the aim is to learn and improve a multilingual AMR parser
by using an existing English parser as its teacher. We constrain our
exploration in a strict multilingual setting: there is but one model to parse
all different languages including English. We identify that noisy input and
precise output are the key to successful distillation. Together with extensive
pre-training, we obtain an AMR parser whose performances surpass all previously
published results on four different foreign languages, including German,
Spanish, Italian, and Chinese, by large margins (up to 18.8 \textsc{Smatch}
points on Chinese and on average 11.3 \textsc{Smatch} points). Our parser also
achieves comparable performance on English to the latest state-of-the-art
English-only parser.
