Machine Translation Evaluation with BERT Regressor
Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi
TL;DR
This paper presents a new machine translation evaluation metric based on BERT, achieving state-of-the-art results in segment-level evaluation for English translation pairs.
Contribution
It introduces a BERT-based regression model for automatic translation quality assessment, outperforming existing metrics on WMT-2017 data.
Findings
Achieves state-of-the-art performance on WMT-2017 dataset
Effective at segment-level translation evaluation
Applicable across multiple English translation pairs
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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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Multimodal Machine Learning Applications
MethodsLinear Layer · Residual Connection · Attention Dropout · Linear Warmup With Linear Decay · Weight Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · Dense Connections · Adam · WordPiece · Softmax