Neural Machine Translation (NMT) has now attained state-of-art performance on large-scale data. However, it does not achieve the best translation results on small data sets. Example-Based Machine Translation (EBMT) is an approach to machine translation in which existing examples in a database are retrieved and modified to generate new translations. To combine EBMT with NMT, an architecture based on the Transformer model is proposed. We conduct two experiments respectively using limited amounts of data, one on an English-French bilingual dataset and the other one on a multilingual dataset with six languages (English, French, German, Chinese, Japanese and Russian). On the bilingual task, our method achieves an accuracy of 96.5 and a BLEU score of 98.8. On the multilingual task, it also outperforms OpenNMT in terms of BLEU scores.

Previous works show that discourse analysis benefits from modeling intra- and inter-sentential levels separately, where proper representations for text units of different granularities are desired to capture both the information of the text units and their relation to the context. In this paper, we propose to take advantage of transformers to encode different contextualized representations of units of different levels to dynamically capture the information required for discourse dependency analysis on intra- and inter-sentential levels. Motivated by the observation of writing patterns shared across articles to improve discourse analysis, we propose to design sequence labeling methods to take advantage of such structural information from the context that substantially outperforms traditional direct classification methods. Experiments show that our model achieves state-of-the-art results on both English and Chinese datasets.