77 citations · 319 across the 61 of their papers we have counts for
6 papers · 1 filter
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…
Balancing Cost and Benefit with Tied-Multi Transformers
Raj Dabre, Raphael Rubino, Atsushi Fujita
We propose and evaluate a novel procedure for training multiple Transformers with tied parameters which compresses multiple models into one enabling the dynamic choice of the numbe…
Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
Haiyue Song, Raj Dabre, Zhuoyuan Mao +3
Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks in low-resource settings. However, large monolingual…
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
Haiyue Song, Raj Dabre, Atsushi Fujita +1
Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a language in…
A Comprehensive Survey of Multilingual Neural Machine Translation
Raj Dabre, Chenhui Chu, Anoop Kunchukuttan
We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality…