51 citations · 114 across the 14 of their papers we have counts for
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When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Zhuoyuan Mao, Chenhui Chu, Raj Dabre +3
Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous…
YANMTT: Yet Another Neural Machine Translation Toolkit
Raj Dabre, Eiichiro Sumita
In this paper we present our open-source neural machine translation (NMT) toolkit called "Yet Another Neural Machine Translation Toolkit" abbreviated as YANMTT which is built on to…
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation
Raj Dabre, Atsushi Fujita
In deep neural network modeling, the most common practice is to stack a number of recurrent, convolutional, or feed-forward layers in order to obtain high-quality continuous space…
Simultaneous Multi-Pivot Neural Machine Translation
Raj Dabre, Aizhan Imankulova, Masahiro Kaneko +1
Parallel corpora are indispensable for training neural machine translation (NMT) models, and parallel corpora for most language pairs do not exist or are scarce. In such cases, piv…
Softmax Tempering for Training Neural Machine Translation Models
Raj Dabre, Atsushi Fujita
Neural machine translation (NMT) models are typically trained using a softmax cross-entropy loss where the softmax distribution is compared against smoothed gold labels. In low-res…
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Zhuoyuan Mao, Fabien Cromieres, Raj Dabre +2
Neural machine translation (NMT) needs large parallel corpora for state-of-the-art translation quality. Low-resource NMT is typically addressed by transfer learning which leverages…