Publications (11)
Improving Multilingual Translation by Representation and Gradient Regularization
Yilin Yang, Akiko Eriguchi, Alexandre Muzio +3
Multilingual Neural Machine Translation (NMT) enables one model to serve all translation directions, including ones that are unseen during training, i.e. zero-shot translation. Des…
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
Shuming Ma, Jian Yang, Haoyang Huang +10
Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialize…
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation
Akiko Eriguchi, Melvin Johnson, Orhan Firat +2
Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP).…
Multilingual Extractive Reading Comprehension by Runtime Machine Translation
Akari Asai, Akiko Eriguchi, Kazuma Hashimoto +1
Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduc…
Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?
HyoJung Han, Akiko Eriguchi, Haoran Xu +3
Vocabulary adaptation, which integrates new vocabulary into pre-trained language models, enables expansion to new languages and mitigates token over-fragmentation. However, existin…
Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations
Akiko Eriguchi, Shufang Xie, Tao Qin +1
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment co…
Tree-to-Sequence Attentional Neural Machine Translation
Akiko Eriguchi, Kazuma Hashimoto, Yoshimasa Tsuruoka
Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Neural Text Generation with Artificial Negative Examples
Keisuke Shirai, Kazuma Hashimoto, Akiko Eriguchi +2
Neural text generation models conditioning on given input (e.g. machine translation and image captioning) are usually trained by maximum likelihood estimation of target text. Howev…
X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale
Haoran Xu, Kenton Murray, Philipp Koehn +3
Large language models (LLMs) have achieved remarkable success across various NLP tasks with a focus on English due to English-centric pre-training and limited multilingual data. In…
Learning to Parse and Translate Improves Neural Machine Translation
Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho
There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguis…