The En-Ru Two-way Integrated Machine Translation System Based on Transformer
Machine translation is one of the most popular areas in natural language processing. WMT is a conference to assess the level of machine translation capabilities of organizations around the world, which is the evaluation activity we participated in. In this review we participated in a two-way translation track from Russian to English and English to Russian. We used official training data, 38 million parallel corpora, and 10 million monolingual corpora. The overall framework we use is the Transformer neural machine translation model, supplemented by data filtering, post-processing, reordering and other related processing methods. The BLEU value of our final translation result from Russian to English is 38.7, ranking 5th, while from English to Russian is 27.8, ranking 10th.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Neural Machine Translation with Reordering Embeddings
The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose …
DecoderMachine TranslationSentenceTranslationTowards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high resource source-pivot and pivot-target lang…
Machine TranslationNMTreinforcement-learningReinforcement Learning (RL)+2University of Tsukuba's Machine Translation System for IWSLT20 Open Domain Translation Task
In this paper, we introduce University of Tsukuba{'}s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine tran…
Machine TranslationRerankingTranslationFrom Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation Systems: Survey, Taxonomy and Performance Evaluation
With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translati…
Machine TranslationSign Language TranslationTranslationThe University of Sydney's Machine Translation System for WMT19
This paper describes the University of Sydney's submission of the WMT 2019 shared news translation task. We participated in the Finnish$\rightarrow$English direction and got the best BLEU(33.0) score among all the partic…
Data AugmentationMachine TranslationRerankingTranslation