184 citations · 210 across the 3 of their papers we have counts for
6 papers
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…
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…
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…
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…
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).…
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…