Reducing Hallucinations in Neural Machine Translation with Feature Attribution

Understand

Neural conditional language generation models achieve the state-of-the-art in Neural Machine Translation (NMT) but are highly dependent on the quality of parallel training dataset.

• When trained on low-quality datasets, these models are prone to various error types, including hallucinations, i.e.
• outputs that are fluent, but unrelated to the source sentences.
• These errors are particularly dangerous, because on the surface the translation can be perceived as a correct output, especially if the reader does not understand the source language.