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Paper · 2104.00290 · ACL · 2021

Many-to-English Machine Translation Tools, Data, and Pretrained Models

Jonathan May, Zhao Zhang, Thamme Gowda, Chris Mattmann

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 8 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
thammegowda/mtdata canonical 4 of 4
isi-nlp/nlcodec canonical 3 of 4
FunctionStatusWhere it lives
best_dtype Ran isi-nlp/nlcodec/nlcodec/db/core.py
code served (permissive licence) · get_code("e54b92783a8ddbc7")
clean Ran thammegowda/mtdata/mtdata/sgm.py
code served (permissive licence) · get_code("c6cdc9376ddcef6a")
detect_extension Ran thammegowda/mtdata/mtdata/parser.py
code served (permissive licence) · get_code("280ccbb1b0e4fbd4")
make_n_grams Ran isi-nlp/nlcodec/nlcodec/utils.py
code served (permissive licence) · get_code("b6e62eee451f9729")
part_path_pads Ran isi-nlp/nlcodec/nlcodec/db/core.py
code served (permissive licence) · get_code("f99e72c2983a154b")
right_replace Ran thammegowda/mtdata/mtdata/cache.py
code served (permissive licence) · get_code("ed8430505d05f010")
rreplace Ran thammegowda/mtdata/mtdata/data.py
code served (permissive licence) · get_code("ed598e786b62e136")
subsequent_mask Not yet run isi-nlp/nlcodec/nlcodec/db/batch.py
code served (permissive licence) · get_code("391870b445058aab")

Repositories linked to this paper

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Abstract

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTDATA, NLCODEC, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages. 1

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