BERTScore: Evaluating Text Generation with BERT
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.
This paper presents a simple application of BERT-based contextual embeddings for evaluation of text generation models such as machine translation and image caption generation. An extensive set of experiments have been carried out to show that the proposed BERTScore metric achieves better correlation with human judgments than the existing metrics. Overall, the paper is well-written and the motivations are clear. However, I am not sure about the technical novelty of the paper as the proposed approach is a natural application of BERT along with traditional cosine similarity measures and precision, recall, F1-based computations, and simple IDF-based importance weighting. Other comments: - It would be interesting to see how the proposed metric performs to evaluate paraphrase generation and text simplification models as the models need to follow specific constraints such as semantic equivalence, novelty, simplicity etc. with respect to source and reference sentences. - Another limitation of the proposed metric is memory and time complexity as it takes relatively more time to evaluate the sentences compared to BLEU, as authors acknowledged in Section 5.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
BERTScore: Evaluating Text Generation with BERT
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents a simple application of BERT-based contextual embeddings for evaluation of text generation models such as machine translation and image caption generation. An extensive set of experiments have been carried out to show that the proposed BERTScore metric achieves better correlation with human judgments than the existing metrics. Overall, the paper is well-written and the motivations are clear. However, I am not sure about the technical novelty of the paper as the proposed approach is a natural application of BERT along with traditional cosine similarity measures and precision, recall, F1-based computations, and simple IDF-based importance weighting. Other comments: - It would be interesting to see how the proposed metric performs to evaluate paraphrase generation and text simplification models as the models need to follow specific constraints such as semantic equivalence, novelty, simplicity etc. with respect to source and reference sentences. - Another limitation of the proposed metric is memory and time complexity as it takes relatively more time to evaluate the sentences compared to BLEU, as authors acknowledged in Section 5.
assistant
This paper presents a simple application of BERT-based contextual embeddings for evaluation of text generation models such as machine translation and image caption generation. An extensive set of experiments have been carried out to show that the proposed BERTScore metric achieves better correlation with human judgments than the existing metrics. Overall, the paper is well-written and the motivations are clear. However, I am not sure about the technical novelty of the paper as the proposed approach is a natural application of BERT along with traditional cosine similarity measures and precision, recall, F1-based computations, and simple IDF-based importance weighting. Other comments: - It would be interesting to see how the proposed metric performs to evaluate paraphrase generation and text simplification models as the models need to follow specific constraints such as semantic equivalence, novelty, simplicity etc. with respect to source and reference sentences. - Another limitation of the proposed metric is memory and time complexity as it takes relatively more time to evaluate the sentences compared to BLEU, as authors acknowledged in Section 5.