WMT2017 German-English machine translation challenge for news

Translate news articles from German into English. [ver. 1.0.0]

This is a long list of all submissions, if you want to see only the best, click leaderboard.

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#	submitter	when	ver.	description	dev-0 BLEU	dev-1 BLEU	test-A BLEU
6	[anonymized]	2021-02-17 19:16	1.0.0	result fairseq m2m-100 just-inference	0.33857	0.40306	0.35347
5	[anonymized]	2021-02-12 19:54	1.0.0	notebook fairseq m2m-100 just-inference	0.33857	0.40306	0.35347
4	[anonymized]	2021-02-12 19:53	1.0.0	m2m-100	0.33857	0.40306	0.35347
21	[anonymized]	2020-01-28 17:56	1.0.0	translation with ready made fairseq transformer.wmt19.de-en v2 fairseq ready-made-model	0.23399	0.27485	0.23698
20	[anonymized]	2020-01-27 20:40	1.0.0	translation with ready made fairseq transformer.wmt19.de-en fairseq ready-made-model	N/A	0.27485	0.23698
129	[anonymized]	2020-01-27 12:19	1.0.0	CNN, sample_size = 5mln, epochs = 5 fairseq train	0.07706	0.09276	0.07834
1	[anonymized]	2020-01-15 09:58	1.0.0	Poprawienie Tokenizacji istniejacego rozwiazania v3 ready-made fairseq	0.39610	0.47024	0.41504
2	[anonymized]	2020-01-15 09:29	1.0.0	Poprawienie Tokenizacji istniejacego rozwiazania v2 ready-made fairseq	0.39264	0.46386	0.40909
162	[anonymized]	2020-01-15 09:15	1.0.0	Poprawienie Tokenizacji istniejacego rozwiazania	N/A	N/A	N/A
3	[anonymized]	2020-01-14 20:53	1.0.0	fix tokenization of output ready-made fairseq	0.38549	0.45189	0.39879
10	[anonymized]	2020-01-07 11:35	1.0.0	ready-made Fairseq model fairseq ready-made-model	0.24760	0.31147	0.26579
9	[anonymized]	2019-12-30 09:59	1.0.0	Runed a ready-made Fairseq model fairseq ready-made-model	0.24760	0.31147	0.26579
130	[anonymized]	2019-05-22 21:02	1.0.0	marian 100k tg freq 10000 neural-network marian	0.06805	0.07651	0.06824
120	[anonymized]	2019-05-22 18:44	1.0.0	marian 100k freq 10000 neural-network marian	0.11676	0.13285	0.11359
17	[anonymized]	2019-05-22 12:01	1.0.0	marian 1M neural-network marian	0.23935	0.27904	0.24561
60	[anonymized]	2019-05-22 11:47	1.0.0	marian 1M tg neural-network marian	0.17381	0.20079	0.18072
22	[anonymized]	2019-02-05 11:36	1.0.0	type=s2s, corpseLen=1M, valid-freq 10000, early-stopping 5, workspace 2500, postproc sed deescapeSpecialChars detruecase awk sed neural-network	0.23399	0.27282	0.23674
23	[anonymized]	2019-01-22 17:17	1.0.0	type=amun, corpseLen=1M, valid-freq 10000, early-stopping 5, workspace 2500, postproc sed deescapeSpecialChars detruecase awk sed neural-network	0.23293	0.27193	0.23254
27	[anonymized]	2019-01-12 12:57	1.0.0	corpseLen=590k, valid-freq 10000, early-stopping 5, workspace 3000, postproc sed deescapeSpecialChars detruecase awk sed	0.20598	0.24117	0.21002
28	[anonymized]	2019-01-12 11:20	1.0.0	corpseLen=590k, valid-freq 10000, early-stopping 5, postproc sed deescapeSpecialChars detruecase awk	0.20547	0.24024	0.20857
29	[anonymized]	2019-01-11 10:18	1.0.0	corpseLen=590k, valid-freq 10000, early-stopping 5, postproc sed deescapeSpecialChars detruecase	0.20547	0.24024	0.20857
35	[anonymized]	2019-01-11 10:08	1.0.0	corpseLen=590k, valid-freq 10000, early-stopping 5, postproc sed deescapeSpecialChars	0.19604	0.23046	0.19946
42	[anonymized]	2019-01-11 10:06	1.0.0	corpseLen=590k, valid-freq 10000, early-stopping 5, postproc sed	0.18594	0.22086	0.19142
104	[anonymized]	2019-01-10 22:12	1.0.0	corpseLen=590k, valid-freq 10000, early-stopping 5, no postproc	0.16705	0.20042	0.17246
118	[anonymized]	2019-01-03 22:12	1.0.0	with awk simple postproc on out	0.13057	0.15454	0.12454
126	[anonymized]	2018-12-09 14:47	1.0.0	Second dell commit	0.09811	0.11533	0.09146
11	[anonymized]	2018-02-15 00:38	1.0.0	Tensorflow 80k iterations ; beam 4 alpha 0.9 ready-made neural-network	0.25882	0.30561	0.26322
12	[anonymized]	2018-02-15 00:05	1.0.0	Tensorflow 80k iterations ; beam 3 alpha 0.6 ready-made neural-network	0.25720	0.30334	0.26143
13	[anonymized]	2018-02-14 23:18	1.0.0	Tensorflow 86k iterations ; beam 3 alpha 0.6 ready-made neural-network	0.25409	0.30126	0.25949
18	[anonymized]	2018-02-14 11:47	1.0.0	Tensorflow 50k iterations ; beam 20 alpha 0.6 ready-made neural-network	0.23913	0.28295	0.24414
40	[anonymized]	2018-02-07 11:10	1.0.0	Add 5G data moses	0.19762	0.22481	0.19183
39	[anonymized]	2018-02-07 11:00	1.0.0	Add 5G data	N/A	0.22481	0.19183
58	[anonymized]	2018-02-07 10:51	1.0.0	improve solution -stack 155 moses	0.18141	0.20750	0.18236
57	[anonymized]	2018-02-07 10:47	1.0.0	Improve sollution -stack 155 moses	0.18141	N/A	0.18236
133	[anonymized]	2018-02-04 23:59	1.0.0	'baseline' moses	0.02757	0.02569	0.02823
36	[anonymized]	2018-01-31 11:43	1.0.0	corpus=590616, NB_OF_EPOCHS=8, MAX_WORDS=46000 neural-network	0.18610	0.21637	0.19461
56	[anonymized]	2018-01-24 11:05	1.0.0	improve solution moses	0.18141	N/A	0.18236
113	p/tlen	2018-01-17 06:46	1.0.0	NMT with Marian, vocabulary=70K, epochs=7	0.14849	0.17603	0.15308
75	[anonymized]	2018-01-16 18:36	1.0.0	--search-algorithm 1 -s 2000 --cube-pruning-pop-limit 2000 --cube-pruning-diversity 100-b 0.1 --minimum-bayes-risk moses	0.17767	0.20160	0.17646
110	p/tlen	2018-01-15 09:13	1.0.0	NMT trained with Marian on 10%, 5 epochs, 40K dictionary neural-network	0.15263	0.17750	0.15966
135	[anonymized]	2018-01-14 16:45	1.0.0	'ibm self-made algo	N/A	N/A	0.02608
145	[anonymized]	2018-01-14 16:33	1.0.0	ibm1	N/A	N/A	0.00762
76	[anonymized]	2018-01-13 21:39	1.0.0	Baseline 10%, stack 200 beam 0.1 moses	0.17469	0.19716	0.17625
105	[anonymized]	2018-01-13 21:22	1.0.0		0.17468	0.19761	0.17224
108	[anonymized]	2018-01-13 19:21	1.0.0		0.17009	0.19237	0.16779
132	[anonymized]	2018-01-13 19:12	1.0.0		0.06572	0.07063	0.06317
7	p/tlen	2018-01-09 18:10	1.0.0	WMT16 neural model (decoded with Amun) + de-escape apostrophes neural-network	0.27932	0.33703	0.28988
8	p/tlen	2018-01-08 21:13	1.0.0	neural model (decoded with Amun) neural-network	0.27358	0.33058	0.28454
77	[anonymized]	2018-01-08 18:13	1.0.0		0.17546	0.19893	0.17588
92	[anonymized]	2018-01-08 18:04	1.0.0		0.17546	0.19893	0.17369

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