Neural Machine Translation For Financial Listing Documents

In this paper, we focus on developing a Neural Machine Translation (NMT) system on English-to-Traditional-Chinese translation for financial prospectuses of companies which seek listing on the Hong Kong Stock Exchange. To the best of our knowledge, this is the first work on NMT for this specific domain. We propose a domain-specific NMT system by introducing a domain flag to indicate the target-side domain. By training the NMT model on the data from both the IPO corpus and the general domain corpus, we can expand the vocabulary while capturing the common writing styles and sentence structures. Our experimental results show that the proposed NMT system can achieve a significant improvement on translating the IPO documents. More significantly, through a blind assessment by a translator expert, our system outperforms two mainstream commercial tools, the Google translator and SDL Trado for some IPO documents.