hsing-wang/wmt2020_biomedical

WMT2020_BioMedical_MT

http://www.statmt.org/wmt20/biomedical-translation-task.html

The Tencent AI Lab participated in the WMT20 Shared Task on Biomedical Translation in four language directions: German<->English, Chinese<->English. Our systems in German->Enlgish and English-> German were ranked 1st and 3rd respectively according to the automatic evaluation.

Data and Pre-trained models

Data provided by organizers

No.	Corpus	File	En-Zh	En-De	En
1	Biomedical Translation	wmt18/Medline/training/es-en	n/a	n/a	287811
		wmt18/Medline/training/fr-en	n/a	n/a	627576
		wmt18/Medline/training/pt-en	n/a	n/a	74645
		wmt18/Medline/test	-	-	-
		wmt19/Medline/training/de-en	n/a	40398	40398
		wmt19/Medline/training/fr-en	n/a	n/a	75049
		wmt19/Medline/training/es-en	n/a	n/a	100257
		wmt19/Medline/training/pt-en	n/a	n/a	49918
		wmt19/Medline/test/Medline	-	-	-
		wmt20/Medline/training/it-en	n/a	n/a	14756
		wmt20/Medline/training/ru-en	n/a	n/a	46782
2	UFAL Medical Corpus	shuffled.de-en	n/a	37814533	n/a
3	HimL test sets	khresmoi-summary-dev	n/a	500	n/a
		khresmoi-summary-test	n/a	1000	n/a
4	Khresmoi development data	himl-test-2015/cochrane	n/a	1433	n/a
		himl-test-2015/himl	n/a	3892	n/a
		himl-test-2015/nhs24	n/a	2459	n/a
		himl-test-2017/cochrane	n/a	467	n/a

Model hyperparameters

	Deep Transformer	Hybrid Transformer	Big Transformer	Large Transformer
Encoder Layer	40	40	6	20
Decoder Layer	6	6	6	6
Attention Heads	8	8	16	16
Embedding Size	512	512	1024	1024
FFN Size	2048	2048	4096	4096

Pre-trained models

	Deep Transformer	Hybrid Transformer	Big Transformer	Large Transformer
De->En	download	download	download	download
En->De	download	download	download	download

BPE Model: De-En BPE models

Synthetic Chinese-English bilingual data

Chinese-English Biomedical bilingual data

Training Details

Data Preprocessing

Moses scripts

$lang=en(de)

#Step1. normalize-punctuation

./mosesdecoder-master/scripts/tokenizer/normalize-punctuation.perl -l $lang < data.$lang > data.$lang.norm

#Step2. remove-non-printing-char

./mosesdecoder-master/scripts/tokenizer/remove-non-printing-char.perl < data.$lang.norm > data.$lang.norm.remv

#Step3. tokenize

./mosesdecoder-master/scripts/tokenizer/tokenizer.perl -l $lang -threads 10 < data.$lang.norm.remv > data.$lang.norm.remv.tok

Model: Transformer

Toolkit: Fairseq

Citation

Please kindly cite our paper if you find it helpful:

@inproceedings{wang2020tencent,
  title={Tencent AI Lab machine translation systems for the WMT20 biomedical translation task},
  author={Wang, Xing and Tu, Zhaopeng and Wang, Longyue and Shi, Shuming},
  booktitle={Proceedings of the Fifth Conference on Machine Translation},
  pages={881--886},
  year={2020}
}