Publication at Faculty of Mathematics and Physics |
2020
Abstract
A major challenge in evaluating data-to-text (D2T) generation is measuring the semantic accuracy of the generated text, i.e. its faithfulness to the input data. We propose a new metric for evaluating the semantic accuracy of D2T generation based on a neural model pretrained for natural language inference (NLI).
We use the NLI model to check textual entailment between the input data and the output text in both directions, allowing us to reveal omissions or hallucinations. Input data are converted to text for NLI using trivial templates.
Our experiments on two recent D2T datasets show that our metric can achieve high accuracy in identifying erroneous system outputs.