Timi Fasubaa, Alp Öktem, Bonaventure Dossou, Chris Emezue, Vukosi Marivate, Elan Van Biljon, Julia Kreutzer, Salomey Osei, Herman Kamper, Hady Elsahar, Kelechi Ogueji, Orevaoghene Ahia, and 36 more
We lifted 1 functions out of this paper's own repositories and ran 1 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.
| Repository | Role | Ran |
|---|---|---|
| joeynmt/joeynmt | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| load_config | Ran | joeynmt/joeynmt/joeynmt/config.py code served (permissive licence) · get_code("6da764aa47787f47") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved. "Lowresourced"-ness is a complex problem going beyond data availability and reflects systemic problems in society. In this paper, we focus on the task of Machine Translation (MT), that plays a crucial role for information accessibility and communication worldwide. Despite immense improvements in MT over the past decade, MT is centered around a few highresourced languages. * ∀ to represent the whole Masakhane community.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.02353")
get_code_for_paper("2010.02353")
have("2010.02353")
Connect an agent — have() is free.