The neural-based machine translation (UM-MT) systems, developed by the Natural Language Processing and Portuguese-Chinese Machine Translation (NLP2CT) Laboratory of the University of Macau (UM), recently won the first, second, third, and fifth prizes in the constraint English-to-Chinese machine translation campaign organised under the 13th China Workshop on Machine Translation (CWMT 2017).
This year’s competition received a total of 73 submissions from 18 companies and universities, including Sogou, Toshiba, Beihang University, Xiamen University, and the Chinese Academy of Sciences. The translation tasks of this evaluation campaign involved six language pairs, namely English-to/from-Chinese, Mongolian-to-Chinese, Uyghur-to-Chinese, Tibetan-to-Chinese, and Japanese-to-Chinese, in four translation domains, including news, patents, daily expressions, and government documents. The English-to/from-Chinese translation tasks were co-organised by CWMT2017 and the International Conference on Machine Translation (WMT 2017).
Under the supervision of Associate Professor Derek Wong and Assistant Professor Lidia Chao from the FST, three systems from UM won the top three prizes in the constraint category.
In addition, compared to other systems that were run on large datasets (25 million sentences) provided by both CWMT and WMT, UM’s systems were run on a small dataset (9 million sentences) provided by CWMT only and won the second, third, and fifth prizes. The first prize went to Sogou.
During the conference, Um2T, UM’s online interactive Portuguese-Chinese machine translation system, received positive feedback and recognition from other participants. Um2T is based on the state-of-the-art neural machine translation architecture and technology and has many advanced features. This system is now available online at https://nlp2ct.cis.umac.mo/NMT/ for public use.
The NLP2CT lab places a great emphasis on the training of students in theoretical research and engineering practice. The research achievements have been published in a number of top international journals and at international conferences, including journals and conferences of the Institute of Electrical and Electronics Engineers/Association for Computing Machinery (IEEE/ACM), and the Association for Computational Linguistics (ACL), as well as the Conference on Empirical Methods in National Language Processing (EMNLP), and the International Conference on Computational Linguistics (COLING). The models and algorithms developed by UM have achieved good rankings in many national and international evaluation campaigns, including second places in English and Chinese news translation campaign of CWMT 2015 and first places in English-to-German, Czech-to-English and French-to-English medical translation tasks of WMT 2014.