Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level

TL;DR

This paper enhances Chinese-to-English neural machine translation by integrating Chinese radicals into the model, improving translation adequacy and outperforming baseline systems across multiple evaluation metrics.

Contribution

It introduces a novel method of incorporating Chinese radicals into NMT models, addressing OOV issues and improving translation quality for Chinese.

Findings

Radical-based models outperform baseline in adequacy metrics

Word boundary knowledge is crucial for Chinese NMT performance

Radical integration improves translation of unseen words

Abstract

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 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 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.