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Found 4 papers, 2 papers with code
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
Neural sequence generation models are known to "hallucinate", by producing outputs that are unrelated to the source text.
An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Machine translation systems based on deep neural networks are expensive to train.
Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT
Although measuring intrinsic quality has been a key factor in the advancement of Machine Translation (MT), successfully deploying MT requires considering not just intrinsic quality but also the user experience, including aspects such as trust.
Can Statistical Post-Editing with a Small Parallel Corpus Save a Weak MT Engine?
In this work we conduct a human evaluation of statistical post-edited output from a weak rule-based MT system, comparing the results with the output of the original rule-based system and a phrase-based statistical MT system trained on the same data.
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