Papers with Low-resource machine translation

2 papers
AraBench: Benchmarking Dialectal Arabic-English Machine Translation (2020.coling-main)

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Challenge: Existing efforts to translate Arabic dialects to English are limited due to the lack of evaluation benchmarks.
Approach: They propose an evaluation suite for Arabic to English machine translation using existing Arabic resources.
Outcome: The evaluation suite for Arabic to English machine translation is based on existing evaluation benchmarks.
Translation Memories as Baselines for Low-Resource Machine Translation (2022.lrec-1)

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Challenge: low-resource machine translation research often requires building baselines to benchmark progress in translation quality.
Approach: They argue that using available text as a translation memory baseline is simple and effective . they say that if you have parallel text, you have a TM .
Outcome: a new study shows that using available text as a translation memory baseline is simple and effective . low-resource machine translation is often of too low quality to use directly, the authors argue .

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