Incorporating Chinese radicals into neural machine translation: deeper than character level
Han, LifengORCID: 0000-0002-3221-2185 and Kuang, Shaohui
(2018)
Incorporating Chinese radicals into neural machine translation: deeper than character level.
In: 30th European Summer School in Logic, Language and Information (ESSLLI 2018), 6-17 Aug 2018, Sofia, Bulgaria.
In neural machine translation (NMT), researchers face the challenge of un-seen (or out-of-vocabulary OOV) words translation. To solve this, some researchers propose the splitting of western languages such as English and German into sub-words or compounds. In this paper, we try to address this OOV issue and improve the NMT adequacy with a harder language Chinese whose characters are even more sophisticated in composition. We integrate the Chinese radicals into the NMT model with different settings to address the unseen words challenge in Chinese to English translation. On the other hand, this also can be considered as semantic part of the MT system since the Chinese radicals usually carry the essential meaning of the words they are constructed in. Meaningful radicals and new characters can be integrated into the NMT systems with our models. We use an attention-based NMT system as a strong baseline system. The experiments on standard Chinese-to-English NIST translation shared task data 2006 and 2008 show that our designed models outperform the baseline model in a wide range of state-of-the-art evaluation metrics including LEPOR, BEER, and CharacTER, in addition to the traditional BLEU and NIST scores, especially on the adequacy-level translation.
We also have some interesting findings from the results of our various experiment settings about the performance of words and characters in Chinese NMT, which is different with other languages. For instance, the fully character level NMT may perform very well or the state of the art in some other languages as researchers demonstrated recently, however, in the Chinese NMT model, word boundary knowledge is important for the model learning.
Item Type:
Conference or Workshop Item (Poster)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Chinese radical; Chinese decomposition; Chinese to English Machine Translation; Neural Networks; Deep Learning
Proceedings of 30th European Summer School in Logic, Language and Information (ESSLLI 2018) - Student Session.
(54-65).
Association for Logic, Language and Information (FoLLI).
Publisher:
Association for Logic, Language and Information (FoLLI)