Abstract
Aligning the representation spaces of two languages to induce a bilingual lexicon achieves attractive results on European language pairs. Unfortunately, current solutions perform terribly on distant language pairs. To address this problem, we analyze existing models for the lexicon induction task of distant language pairs, such as English-Chinese. We propose an framework for the task with improved preprocessing, mapping and inference accordingly. Experimental results show that our proposed approach enhances the accuracy of bilingual lexicons substantially on English-Chinese, as well as some other distant language pairs.
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Acknowledgement
We would like to thank the anonymous reviewers for their insightful comments. Shujian Huang is the corresponding author. This work is supported by the National Science Foundation of China (No. 61772261), the Jiangsu Provincial Research Foundation for Basic Research (No. BK20170074), “13th Five-Year” All-Army Common Information System Equipment Pre-Research Project (No. 31510040201). This work is also partially supported by the research funding from ZTE Corporation.
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Zhu, W. et al. (2019). Improving Bilingual Lexicon Induction on Distant Language Pairs. In: Huang, S., Knight, K. (eds) Machine Translation. CCMT 2019. Communications in Computer and Information Science, vol 1104. Springer, Singapore. https://doi.org/10.1007/978-981-15-1721-1_1
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DOI: https://doi.org/10.1007/978-981-15-1721-1_1
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