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main_email:yanghao30@huawei.com
Presentation Submission Go to [Presentation]
Work Method
* Looking though a Presentation Sample (click!!) as the target.
Step.1: Submit a slide (select slide number + upload .jpg + description) + Write button (Save)
Step.2: Review a submitted slide with .jpg and description, and listen text to speech function
Step.3: Any time, edit it by selecting the slide hyper link on top a slide + Write button (Save)
*Give it a try right away!!
Paper Number
Paper Title
Keyword
Online Presentation * Submit YouTube URL: Compulsory for online participant!
Slide Number * Upload slide file one by one, Notice: .jpg(O) .JPG(X), click -> 10.jpg  
* Min. 20 ~ Max. 40 slides!!
Slide Display
Verbal Description
*Must fill up in details as much as you can do!
Save the slide and description

*** You can edit any slide by selecting the Slide number below***
ICACT20230134 Slide.15        [Big slide for presentation]       Chrome Text-to-Speach Click!!
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

ICACT20230134 Slide.14        [Big slide for presentation]       Chrome Text-to-Speach Click!!
TeacherSim achieves SOTA performance for cross-lingual machine translation evaluation Two student embeddings are aligned with monolingual teacher embedding Parallel corpora and sentence-level evaluation can be used together Future works will major in more languages and more domains, like BLEURT

ICACT20230134 Slide.13        [Big slide for presentation]       Chrome Text-to-Speach Click!!
Visualization for Token-level Alignment XLM-R VS. TeacherSim Last layer selected

ICACT20230134 Slide.12        [Big slide for presentation]       Chrome Text-to-Speach Click!!
Visualization for Token-level Alignment XLM-R VS. TeacherSim Last layer selected

ICACT20230134 Slide.11        [Big slide for presentation]       Chrome Text-to-Speach Click!!
TeacherSim (19%) Outperforms mBERT (11%), LASER (15%), and XMoverScore (15%) TeacherSim+LM (29%) Surpasses TeacherSim (19%) and XMoverScore+LM (27%)

ICACT20230134 Slide.10        [Big slide for presentation]       Chrome Text-to-Speach Click!!
TeacherSim (19%) Outperforms mBERT (11%), LASER (15%), and XMoverScore (15%)

ICACT20230134 Slide.09        [Big slide for presentation]       Chrome Text-to-Speach Click!!
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

ICACT20230134 Slide.08        [Big slide for presentation]       Chrome Text-to-Speach Click!!
Training Process Minimize two MSE losses Best performance for Siamese Network than Classification/Regression losses Evaluation Process Cosine similarity, simple but accurate

ICACT20230134 Slide.07        [Big slide for presentation]       Chrome Text-to-Speach Click!!
Monolingual embedding as teacher Cross-lingual embeddings as two students Training with aligned corpus MSE-loss for Teacher and Student EN MSE-loss for Teacher and Student ZH Evaluating with sentence pair Student EN Embedding Student ZH Embedding Cosine Similarity as Evaluation Score

ICACT20230134 Slide.06        [Big slide for presentation]       Chrome Text-to-Speach Click!!
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

ICACT20230134 Slide.05        [Big slide for presentation]       Chrome Text-to-Speach Click!!
XMoverScore: Token-level, not sentence-level

ICACT20230134 Slide.04        [Big slide for presentation]       Chrome Text-to-Speach Click!!
mBERT & Laser: No parallel corpora training XMoverScore: Token-level, not sentence-level

ICACT20230134 Slide.03        [Big slide for presentation]       Chrome Text-to-Speach Click!!
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

ICACT20230134 Slide.02        [Big slide for presentation]       Chrome Text-to-Speach Click!!
TeacherSim architecture Cross-lingual sentence-level representation Monolingual sentence representation as teacher Fine-tuning with parallel corpora, delivering SOTA performance

ICACT20230134 Slide.01        [Big slide for presentation]       Chrome Text-to-Speach Click!!
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

ICACT20230134 Slide.00        [Big slide for presentation]       Chrome Text-to-Speach Click!!
TeacherSim: Cross-lingual Machine Translation Evaluation with Monolingual Embedding as Teacher