Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation

ICLR 2020 Ran TianShashi NarayanThibault SellamAnkur P. Parikh

Neural conditional text generation systems have achieved significant progress in recent years, showing the ability to produce highly fluent text. However, the inherent lack of controllability in these systems allows them to hallucinate factually incorrect phrases that are unfaithful to the source, making them often unsuitable for many real world systems that require high degrees of precision... (read more)

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