51 citations · 141 across the 13 of their papers we have counts for
12 papers · 1 filter
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…
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation
Zhuoyuan Mao, Chenhui Chu, Sadao Kurohashi
In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for la…
A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences
Andrew Merritt, Chenhui Chu, Yuki Arase
Multimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more…
Lexically Cohesive Neural Machine Translation with Copy Mechanism
Vipul Mishra, Chenhui Chu, Yuki Arase
Lexically cohesive translations preserve consistency in word choices in document-level translation. We employ a copy mechanism into a context-aware neural machine translation model…
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…
Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
Chenhui Chu, Raj Dabre
In this paper, we propose two novel methods for domain adaptation for the attention-only neural machine translation (NMT) model, i.e., the Transformer. Our methods focus on trainin…