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.
@article{arxiv.2109.15196,
title = {Multilingual AMR Parsing with Noisy Knowledge Distillation},
author = {Deng Cai and Xin Li and Jackie Chun-Sing Ho and Lidong Bing and Wai Lam},
journal= {arXiv preprint arXiv:2109.15196},
year = {2021}
}