BERTScore: Evaluating Text Generation with BERT

Abstract

We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics. Finally, we use an adversarial paraphrase detection task and show that BERTScore is more robust to challenging examples compared to existing metrics.

Keywords

Security tokenComputer scienceMachine translationParaphraseArtificial intelligenceSentenceMetric (unit)Similarity (geometry)Natural language processingClosed captioningTask (project management)Selection (genetic algorithm)Text generationMachine learningSpeech recognitionImage (mathematics)

Affiliated Institutions

Cornell University US

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Publication Info

Year: 2020
Type: article
Citations: 603
Access: Closed

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APA Style

                            
                                    Tianyi Zhang, 
                                
                                    Varsha Kishore, 
                                
                                    Felix Wu
                                
                                et al.
                            
                            (2020). 
                            BERTScore: Evaluating Text Generation with BERT. 
                            arXiv (Cornell University)
                            
                            .