BERTScore Evaluation Metric

A BERTScore Evaluation Metric is a natural language generation (NLG) performance measure that leverages pre-trained contextual embeddings from BERT language models to compute semantic similarity scores between candidate texts and reference texts.

• AKA: BERTScore, BERT-Based Evaluation Metric, BERT-Based NLG Performance Measure, Contextual Embedding-Based NLG Metric.
• Context:
  • It can typically compute NLG token-level similarity scores using contextual embeddings rather than exact string matching.
  • It can typically generate NLG precision scores, NLG recall scores, and NLG F1 scores by aggregating token-level similaritys.
  • It can typically measure NLG semantic adequacy through contextual similarity computations between candidate tokens and reference tokens.
  • It can often outperform traditional n-gram-based NLG performance measures like BLEU Score and ROUGE Score in human correlation studies.
  • It can often leverage different pre-trained BERT variants including BERT, RoBERTa, ALBERT, and XLM-RoBERTa for multilingual NLG evaluation.
  • It can often apply importance weighting using inverse document frequency (IDF) to emphasize rare words over common words.
  • It can handle NLG semantic paraphrases and NLG syntactic variations better than surface-level NLG metrics through contextual understanding.
  • It can compute greedy matching between NLG candidate tokens and NLG reference tokens using cosine similarity of their contextual embeddings.
  • It can optionally rescale raw BERTScore values to improve NLG metric interpretability using baseline rescaling.
  • It can support multi-reference NLG evaluation by computing maximum similarity across multiple reference texts.
  • It can be computationally more expensive than string-matching NLG performance measures due to neural model inference.
  • It can range from being a Simple BERTScore Implementation to being a Production-Ready BERTScore System, depending on its optimization level.
  • It can range from being a Monolingual BERTScore Evaluation Metric to being a Multilingual BERTScore Evaluation Metric, depending on its language support.
  • It can integrate with NLG evaluation frameworks like HuggingFace Evaluate and NLTK for standardized NLG evaluation.
  • ...
• Example(s):
  • BERTScore Implementation Versions, such as:
    • BERTScore v0.3.0+, which introduced IDF weighting and baseline rescaling.
    • Multilingual BERTScore, using XLM-RoBERTa for cross-lingual NLG evaluation.
  • BERTScore Application Domains, such as:
    • Machine Translation BERTScore, where it correlates better with human judgments than BLEU Score.
    • Summarization BERTScore, capturing semantic equivalence in abstractive summarization.
    • Image Captioning Evaluation, assessing semantic accuracy of generated captions.
    • Dialogue BERTScore, measuring response quality in conversational AI systems.
    • Paraphrase Detection BERTScore, identifying semantically equivalent texts with syntactic variations.
  • BERTScore Configurations, such as:
    • Layer-Specific BERTScore, using embeddings from specific transformer layers (typically layer 9-12).
    • IDF-Weighted BERTScore, applying corpus-based importance weights.
    • Rescaled BERTScore, normalizing scores using empirical baselines.
  • BERTScore Research Studies, such as:
    • Zhang et al., 2019, the original BERTScore paper introducing the metric.
    • Hanna & Bojar, 2021, analyzing BERTScore strengths and BERTScore weaknesses.
  • ...
• Counter-Example(s):
  • BLEU Score, which relies on exact n-gram matching without semantic understanding.
  • ROUGE Score, which focuses on recall-oriented n-gram overlap without contextual embeddings.
  • METEOR Score, which uses WordNet synonyms but lacks deep contextual representations.
  • Human-Based NLG Performance Measure, which provides subjective quality assessments rather than automatic scoring.
  • Perplexity Metric, which measures language model confidence rather than reference similarity.
  • Reference-Free NLG Performance Measure, which evaluates without gold references unlike BERTScore's reference-based approach.
• See: Natural Language Generation (NLG) Performance Measure, Automatic NLG Performance Measure, Reference-Based NLG Performance Measure, BERT Language Model, Contextual Embedding, Semantic Similarity Measure, Machine Translation Performance Measure, Text Generation Quality Metric, Neural Evaluation Metric, Cosine Similarity, Token-Level Similarity, Automated Text Quality Assessment.

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References

2023

• GBard
  • BERTScore is an evaluation metric for text generation that uses the contextual embeddings from BERT to compute similarity between candidate and reference texts. It was first proposed in the paper BERTScore: Evaluating Text Generation with BERT: https://arxiv.org/abs/1904.09675 by Zhang et al. (2020).
  • BERTScore is a more comprehensive evaluation metric than traditional metrics like BLEU, which only consider exact matches between candidate and reference texts. BERTScore is able to capture more nuanced similarities between texts, such as semantic similarity and syntactic similarity. This makes it a more reliable metric for evaluating the quality of text generation.
  • Here are some of the advantages of using BERTScore:
    • It is more comprehensive than traditional metrics like BLEU.
    • It is able to capture more nuanced similarities between texts.
    • It is reliable and consistent.
    • It is easy to use.
  • Here are some of the disadvantages of using BERTScore:
    • It is computationally expensive.
    • It requires a large amount of data to train.
    • It is not always clear how to interpret the results.

2021

• (Hanna & Bojar, 2021) ⇒ Michael Hanna, and Ondřej Bojar. (2021). “A Fine-grained Analysis of BERTScore.” In: Proceedings of the Sixth Conference on Machine Translation.
  • ABSTRACT: BERTScore, a recently proposed automatic metric for machine translation quality, uses BERT, a large pre-trained language model to evaluate candidate translations with respect to a gold translation. Taking advantage of BERT’s semantic and syntactic abilities, BERTScore seeks to avoid the flaws of earlier approaches like BLEU, instead scoring candidate translations based on their semantic similarity to the gold sentence. However, BERT is not infallible; while its performance on NLP tasks set a new state of the art in general, studies of specific syntactic and semantic phenomena have shown where BERT’s performance deviates from that of humans more generally. This naturally raises the questions we address in this paper: what are the strengths and weaknesses of BERTScore? Do they relate to known weaknesses on the part of BERT? We find that while BERTScore can detect when a candidate differs from a reference in important content words, it is less sensitive to smaller errors, especially if the candidate is lexically or stylistically similar to the reference.

2019

• (Zhang et al., 2019) ⇒ Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. (2019). “Bertscore: Evaluating Text Generation with Bert.” arXiv preprint arXiv:1904.09675.
  • 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 to show that BERTScore is more robust to challenging examples when compared to existing metrics.

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