A major issue in machine translation (MT) applications is the recognition and translation of named entities. This is especially true for Chinese and Japanese, whose scripts present linguistic and algorithmic challenges not found in other languages. This paper discusses some of the major issues in Japanese and Chinese MT, such as the difficulties of translating proper nouns and technical terms, and the complexities of orthographic variation in Japanese. Of special interest are neural machine translation (NMT) systems, which suffer from a serious out-of-vocabulary problem. However, the current architecture of these systems makes it technically challenging for them to alleviate this problem by supporting lexicons. This paper introduces some Very Large-Scale Lexical Resources (VLSLR) consisting of millions of named entities, and argues that the quality of MT in general, and NMT systems in particular, can be significantly enhanced through the integration of lexicons.
@InProceedings{HALPERN18.246, author = {Jack Halpern}, title = "{Very Large-Scale Lexical Resources to Enhance Chinese and Japanese Machine Translation}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper