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Neural knowledge-to-text generation models often struggle to faithfullygenerate descriptions for the input facts: they may produce hallucinations thatcontradict the given facts, or describe facts not present in the input. Toreduce hallucinations, we propose a novel decoding method, TWEAK (Think WhileEffectively Articulating Knowledge). TWEAK treats the generated sequences ateach decoding step and its future sequences as hypotheses, and ranks eachgeneration candidate based on how well their corresponding hypotheses supportthe input facts using a Hypothesis Verification Model (HVM). We firstdemonstrate the effectiveness of TWEAK by using a Natural Language Inference(NLI) model as the HVM and report improved faithfulness with minimal impact onthe quality. We then replace the NLI model with our task-specific HVM trainedwith a first-of-a-kind dataset, FATE (Fact-Aligned Textual Entailment), whichpairs input facts with their faithful and hallucinated descriptions with thehallucinated spans marked. The new HVM improves the faithfulness and thequality further and runs faster. Overall the best TWEAK variants improve onaverage 2.22/7.17 points on faithfulness measured by FactKB over WebNLG andTekGen/GenWiki, respectively, with only 0.14/0.32 points degradation on qualitymeasured by BERTScore over the same datasets. Since TWEAK is a decoding-onlyapproach, it can be integrated with any neural generative model withoutretraining.

Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation