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Machine Translation Evaluation with BERT Regressor

2019/07/29 by Hiroki Shimanaka, Shimanaka, Hiroki, Tomoyuki Kajiwara +3
Computer Science · Engineering · #Artificial intelligence #BLEU #Computation and Language (cs.CL) #Computer science #Encoder #Engineering #FOS: Computer and information sciences #Machine learning #Machine translation #Metric (unit) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Natural language processing #Operating system #Speech recognition #Task (project management) #Topic Modeling #Transformer #Translation (biology) #Voltage #cs.CL

paper · pdf · doi:10.48550/arxiv.1907.12679

6 pages

arxiv created 2019/07/29 · openalex publication_date 2019/07/29 · arxiv updated 2019/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce the metric using BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) for automatic machine translation evaluation. The experimental results of the WMT-2017 Metrics Shared Task dataset show that our metric achieves state-of-the-art performance in segment-level metrics task for all to-English language pairs.

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