Papers with MTNT

2 papers
MTNT: A Testbed for Machine Translation of Noisy Text (D18-1)

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Challenge: Noisy input text can cause disastrous mistranslations in most modern machine translation systems.
Approach: They propose a benchmark dataset for Machine Translation of Noisy Text (MTNT) they use reddit comments and professionally sourced translations to examine noise types.
Outcome: The proposed dataset can provide an attractive testbed for noise-robust machine translation systems.
Ask Language Model to Clean Your Noisy Translation Data (2023.findings-emnlp)

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Challenge: Neural machine translation models exhibit a noticeable decline in translation quality when exposed to noisy input.
Approach: They use a dataset to evaluate the robustness of NMT models against noisy inputs.
Outcome: The proposed dataset cleaners the noise from the target sentences while preserving the semantic integrity of the original sentences.

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