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IEEE/ICACT20230134 Slide.09        [Big Slide]	Oral Presentation
	Evaluation of cross-lingual machine translation problems Pre-trained mBERT, trained with cross-lingual sentences without parallel corpora XMoverScore, calculated at a token level, not sentence level Evaluators like BERTScore need manual layer selection TeacherSim architecture Cross-lingual sentence-level representation Monolingual sentence representation as teacher Fine-tuning with parallel corpora, delivering SOTA performance Experiments TeacherSim achieves SOTA performance for cross-lingual evaluation TeacherSim is easy to use, with last layer always selected, like SBERT TeacherSim is much more accurate for token-level alignment

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