In this work, we present the construction of multilingual parallel corpora
with annotation of multiword expressions (MWEs). MWEs include verbal MWEs
(vMWEs) defined in the PARSEME shared task that have a verb as the head of the
studied terms. The annotated vMWEs are also bilingually and multilingually
aligned manually. The languages covered include English, Chinese, Polish, and
German. Our original English corpus is taken from the PARSEME shared task in
2018. We performed machine translation of this source corpus followed by human
post editing and annotation of target MWEs. Strict quality control was applied
for error limitation, i.e., each MT output sentence received first manual post
editing and annotation plus second manual quality rechecking. One of our
findings during corpora preparation is that accurate translation of MWEs
presents challenges to MT systems. To facilitate further MT research, we
present a categorisation of the error types encountered by MT systems in
performing MWE related translation. To acquire a broader view of MT issues, we
selected four popular state-of-the-art MT models for comparisons namely:
Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the
noise removal, translation post editing and MWE annotation by human
professionals, we believe our AlphaMWE dataset will be an asset for
cross-lingual and multilingual research, such as MT and information extraction.
Our multilingual corpora are available as open access at
github.com/poethan/AlphaMWE.

本研究旨在构建带有多语言多词表达注释的多语言平行语料库，发现机器翻译系统在处理多词表达翻译时存在的误译类型，并总结出一些错误限制规则以提高翻译质量，此平行语料库可用于机器翻译和信息提取的跨语言和多语言的研究。