WMT17

Existing metrics to evaluate the quality of Machine Translation hypotheses take dif- ferent perspectives into account. DPM- Fcomb, a metric combining the merits of a range of metrics, achieved the …...

In this paper we report on FBK’s par- ticipation to the English-to-German news translation task of the Second Conference on Machine Translation (WMT’17). The submitted system is based on Neural …...

Machine translation quality estimation is a challenging task in the WMT evaluation campaign. Feature extrac- tion plays an important role in auto- matic quality estimation, and in this paper, we …...

This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two mul- timodal architectures …...

In this paper, we present a two-stage neu- ral quality estimation model that uses mul- tilevel task learning for translation quality estimation (QE) at the sentence, word, and phrase levels. …...

This paper describes the University of Sheffield’s submission to the WMT17 Multimodal Machine Translation shared task. We participated in Task 1 to de- velop an MT system to translate an …...

This paper describes the AFRL-MITLL machine translation systems and the im- provements that were developed during the WMT17 evaluation campaign. This year, we explore the continuing prolifera- tion of Neural …...

This paper describes the Neural Machine Translation systems of Xiamen University for the translation tasks of WMT 17. Our systems are based on the Encoder-Decoder framework with attention. We partici- …...

AbstractWhile target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated. In this …...

AbstractRecently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal that the lack of recurrence modeling …...

AbstractA neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this …...

AbstractPosition encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. …...

AbstractWe propose a data augmentation method for neural machine translation. It works by interpreting language models and phrasal alignment causally. Specifically, it creates augmented parallel translation corpora by generating (path-specific) …...

AbstractIn supervised learning, a well-trained model should be able to recover ground truth accurately, i.e. the predicted labels are expected to resemble the ground truth labels as much as possible. …...

AbstractWe introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize SimCut, …...

AbstractTransfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods for NMT are static, …...

AbstractThis paper describes our submission to the WMT2022 shared metrics task. Our unsupervised metric estimates the translation quality at chunk-level and sentence-level. Source and target sentence chunks are retrieved by …...