We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh. Since Romansh is an unseen language, we are dealing with a zero-shot setting. Using embeddings from mBERT, both models reach an alignment error rate of 0.22, which outperforms fast_align, a statistical model, and is on par with similarity-based word alignment for seen languages. We interpret these results as evidence that mBERT contains information that can be meaningful and applicable to Romansh. To evaluate performance, we also present a new trilingual corpus, which we call the DERMIT (DE-RM-IT) corpus, containing press releases made by the Canton of Grisons in German, Romansh and Italian in the past 25 years. The corpus contains 4 547 parallel documents and approximately 100 000 sentence pairs in each language combination. We additionally present a gold standard for German-Romansh word alignment. The data is available at https://github.com/eyldlv/DERMIT-Corpus.

翻译：我们测试了基于相似度的词对齐模型（SimAlign和awesome-align）与mBERT及XLM-R的词嵌入在德语-罗曼什语平行句对上的表现。由于罗曼什语是未见语言，本研究属于零样本场景。使用mBERT的嵌入时，两种模型的对齐错误率均达到0.22，优于统计模型fast_align，且与可见语言上基于相似度的词对齐性能持平。我们将此结果解读为：mBERT包含对罗曼什语具有意义且可适用的信息。为评估性能，我们同时构建了一个新的三语语料库——DERMIT（DE-RM-IT）语料库，包含过去25年间格劳宾登州政府发布的德语、罗曼什语及意大利语新闻稿。该语料库包含4547篇平行文档，每种语言组合约有10万个句子对。我们还提供了德语-罗曼什语词对齐的黄金标准参考数据。数据详见https://github.com/eyldlv/DERMIT-Corpus。