  Transformer models have demonstrated remarkable performance in neural machine
translation (NMT). However, their vulnerability to noisy input poses a
significant challenge in practical implementation, where generating clean
output from noisy input is crucial. The MTNT dataset is widely used as a
benchmark for evaluating the robustness of NMT models against noisy input.
Nevertheless, its utility is limited due to the presence of noise in both the
source and target sentences. To address this limitation, we focus on cleaning
the noise from the target sentences in MTNT, making it more suitable as a
benchmark for noise evaluation. Leveraging the capabilities of large language
models (LLMs), we observe their impressive abilities in noise removal. For
example, they can remove emojis while considering their semantic meaning.
Additionally, we show that LLM can effectively rephrase slang, jargon, and
profanities. The resulting datasets, called C-MTNT, exhibit significantly less
noise in the target sentences while preserving the semantic integrity of the
original sentences. Our human and GPT-4 evaluations also lead to a consistent
conclusion that LLM performs well on this task. Lastly, experiments on C-MTNT
showcased its effectiveness in evaluating the robustness of NMT models,
highlighting the potential of advanced language models for data cleaning and
emphasizing C-MTNT as a valuable resource.
