EMNLP Findings2023
CCEval: A Representative Evaluation Benchmark for the Chinese-centric Multilingual Machine Translation
Lianzhang Lou, Xi Yin, Yutao Xie, Yang Xiang
Abstract
The Chinese-centric Multilingual Machine Translation (MMT) has gained more importance recently due to increasing demands from international business development and crosscultural exchanges. However, an important factor that limits the progress of this area is the lack of highly representative and highquality evaluation benchmarks. To fill this gap, we propose CCEval, an impartial and representative Chinese-centric MMT evaluation dataset. This benchmark dataset consists of 2500 Chinese sentences we meticulously selected and processed, and covers more diverse linguistic features as compared to other MMT evaluation benchmarks. These sentences have been translated into 11 languages of various resource levels by professional translators via a rigorously controlled process pipeline to ensure their high quality. We conduct experiments to demonstrate our sampling methodology's effectiveness in constructing evaluation datasets strongly correlated with human evaluations. The resulting dataset enables better assessments of the Chinese-centric MMT quality. Our CCEval benchmark dataset is available at https://bright.pcl.ac.cn/en/ offlineTasks.
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Builds on4
- BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger et al.ICLR 2020 · 8,443 citations
- Improving Massively Multilingual Neural Machine Translation and Zero-Shot TranslationBiao Zhang, Philip Williams, Ivan Titov, Rico SennrichACL 2020 · 213 citations
- Pre-training Multilingual Neural Machine Translation by Leveraging Alignment InformationZehui Lin, Xiao Pan, Mingxuan Wang, Xipeng Qiu et al.EMNLP 2020 · 82 citations
- CCAligned: A Massive Collection of Cross-Lingual Web-Document PairsAhmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, Philipp KoehnEMNLP 2020 · 6 citations
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