An Automatic Quality Metric for Evaluating Simultaneous Interpretation
This addresses the challenge of latency-quality trade-offs in simultaneous interpretation for distant word order language pairs, such as English-Japanese, which is incremental for computational SI and SiMT.
The paper tackles the problem of evaluating simultaneous interpretation by proposing an automatic metric that focuses on word order synchronization between source and target languages, showing effectiveness on datasets like NAIST-SIC-Aligned and JNPC.
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.