BERTScoreoch

BERTScoreoch is a theoretical extension of the BERTScore evaluation metric used in natural language processing to assess the similarity between a generated text and a reference text. In this framework, the term och stands for occlusion-based weighting and cross-lingual alignment, indicating two additional components proposed to improve robustness and multilingual applicability over the original BERTScore.

Methodology: Like BERTScore, BERTScoreoch relies on contextual embeddings from a pre-trained language model to compute token-level similarity between candidate and reference tokens. The occlusion component introduces perturbations by masking selected tokens in the reference or candidate and measuring how the similarity scores change in response. The resulting occlusion-weighted scores are combined with the original token similarities, producing a final score that reflects both lexical-semantic similarity and sensitivity to context perturbations. A cross-lingual alignment module may be included to map tokens across languages using multilingual embeddings, enabling evaluation across language pairs.

Advantages and limitations: Proponents argue that BERTScoreoch can provide more robust evaluations in the presence of noise, paraphrase, or translation shifts, especially in multilingual settings. Critics note increased computational cost and dependence on the choice of perturbation strategy and language model, which can affect comparability across studies. There is not yet a standard benchmark or widely accepted implementation. Related metrics include BERTScore, BLEU, ROUGE, and METEOR.