2023
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Morphological and Semantic Evaluation of Ancient Chinese Machine Translation
Kai Jin
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Dan Zhao
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Wuying Liu
Proceedings of the Ancient Language Processing Workshop
Machine translation (MT) of ancient Chinese texts presents unique challenges due to the complex grammatical structures, cultural nuances, and polysemy of the language. This paper focuses on evaluating the translation quality of different platforms for ancient Chinese texts using The Analects as a case study. The evaluation is conducted using the BLEU, LMS, and ESS metrics, and the platforms compared include three machine translation platforms (Baidu Translate, Bing Microsoft Translator, and DeepL), and one language generation model ChatGPT that can engage in translation endeavors. Results show that Baidu performs the best, surpassing the other platforms in all three metrics, while ChatGPT ranks second and demonstrates unique advantages. The translations generated by ChatGPT are deemed highly valuable as references. The study contributes to understanding the challenges of MT for ancient Chinese texts and provides insights for users and researchers in this field. It also highlights the importance of considering specific domain requirements when evaluating MT systems.
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Multiloop Incremental Bootstrapping for Low-Resource Machine Translation
Wuying Liu
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Wei Li
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Lin Wang
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
Due to the scarcity of high-quality bilingual sentence pairs, some deep-learning-based machine translation algorithms cannot achieve better performance in low-resource machine translation. On this basis, we are committed to integrating the ideas of machine learning algorithm improvement and data augmentation, propose a novel multiloop incremental bootstrapping framework, and design the corresponding semi-supervised learning algorithm. This framework is a meta-frame independent of specific machine translation algorithms. This algorithm makes full use of bilingual seed data of appropriate scale and super-large-scale monolingual data to expand bilingual sentence pair data incrementally, and trains machine translation models step by step to improve the translation quality. The experimental results of neural machine translation on multiple language pairs prove that our proposed framework can make use of continuous monolingual data to raise itself. Its effectiveness is not only reflected in the easy implementation of state-of-the-art low-resource machine translation, but also in the practical option to quickly establish precise domain machine translation systems.
2016
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How does Dictionary Size Influence Performance of Vietnamese Word Segmentation?
Wuying Liu
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Lin Wang
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Vietnamese word segmentation (VWS) is a challenging basic issue for natural language processing. This paper addresses the problem of how does dictionary size influence VWS performance, proposes two novel measures: square overlap ratio (SOR) and relaxed square overlap ratio (RSOR), and validates their effectiveness. The SOR measure is the product of dictionary overlap ratio and corpus overlap ratio, and the RSOR measure is the relaxed version of SOR measure under an unsupervised condition. The two measures both indicate the suitable degree between segmentation dictionary and object corpus waiting for segmentation. The experimental results show that the more suitable, neither smaller nor larger, dictionary size is better to achieve the state-of-the-art performance for dictionary-based Vietnamese word segmenters.
2015
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Russian-Chinese Sentence-level Aligned News Corpus
Wenjun Du
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Wuying Liu
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Junting Yu
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Mianzhu Yi
Proceedings of the 18th Annual Conference of the European Association for Machine Translation
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Russian-Chinese Sentence-level Aligned News Corpus
Wenjun Du
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Wuying Liu
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Junting Yu
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Mianzhu Yi
Proceedings of the 18th Annual Conference of the European Association for Machine Translation