An Automatic Quality Metric for Evaluating Simultaneous Interpretation
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Abstract (arXiv)
Simultaneous interpretation (SI), the translation of one language to another in real time, starts translation before the original speech has finished. Its evaluation needs to consider both latency and quality. This trade-off is challenging especially for distant word order language pairs such as English and Japanese. To handle this word order gap, interpreters maintain the word order of the source language as much as possible to keep up with original language to minimize its latency while maintaining its quality, whereas in translation reordering happens to keep fluency in the target language. This means outputs synchronized with the source language are desirable based on the real SI situation, and it's a key for further progress in computational SI and simultaneous machine translation (SiMT). In this work, we propose an automatic evaluation metric for SI and SiMT focusing on word order synchronization. Our evaluation metric is based on rank correlation coefficients, leveraging cross-lingual pre-trained language models. Our experimental results on NAIST-SIC-Aligned and JNPC showed our metrics' effectiveness to measure word order synchronization between source and target language.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 9 Jul 2024.
Builds on
8 of 24 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
- 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
- 2020SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddingscited by 155
- 2019Monotonic Infinite Lookback Attention for Simultaneous Machine Translationcited by 103
- 2016Learning to Translate in Real-time with Neural Machine Translationcited by 97
- 2022Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translationcited by 56
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