KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation
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Abstract (arXiv)
BERTScore is an effective and robust automatic metric for referencebased machine translation evaluation. In this paper, we incorporate multilingual knowledge graph into BERTScore and propose a metric named KG-BERTScore, which linearly combines the results of BERTScore and bilingual named entity matching for reference-free machine translation evaluation. From the experimental results on WMT19 QE as a metric without references shared tasks, our metric KG-BERTScore gets higher overall correlation with human judgements than the current state-of-the-art metrics for reference-free machine translation evaluation.1 Moreover, the pre-trained multilingual model used by KG-BERTScore and the parameter for linear combination are also studied in this paper.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 30 Jan 2023.
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10 of 30 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
- 2021A Unified Generative Framework for Various NER Subtaskscited by 141
- 2016Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignmentcited by 128
- 2020TransQuest: Translation Quality Estimation with Cross-lingual Transformerscited by 59
- 2019OpenKiwi: An Open Source Framework for Quality Estimationcited by 49
- 2020KoBE: Knowledge-Based Machine Translation Evaluationcited by 6
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.