activity
20182026
most citedExploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation

15 citations · 30 across the 12 of their papers we have counts for

collaborators

16 papers

cs.CL2026

A Study on Question-Answer Dataset for LLM Safety Evaluation with a Focus on Illegal Activities

Kenji Imamura, Masao Ideuchi, Atsushi Fujita

In this paper, we discuss question-answer dataset for LLM safety evaluation, with a focus on illegal activities. Specifically, on the basis of manual analysis of AnswerCarefully, w…

cs.CL2026

ATD-Trans: A Geographically Grounded Japanese-English Travelogue Translation Dataset

Shohei Higashiyama, Hiroki Ouchi, Atsushi Fujita +1

Geographic text, or textual data rich in geographic (geo-) information is a valuable source for various geographic applications, e.g., tourism management. Making such information a…

cs.CL2023

Unsupervised Translation Quality Estimation Exploiting Synthetic Data and Pre-trained Multilingual Encoder

Yuto Kuroda, Atsushi Fujita, Tomoyuki Kajiwara +1

Translation quality estimation (TQE) is the task of predicting translation quality without reference translations. Due to the enormous cost of creating training data for TQE, only…

cs.CL2023

Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts

Haiyue Song, Raj Dabre, Chenhui Chu +2

Lecture transcript translation helps learners understand online courses, however, building a high-quality lecture machine translation system lacks publicly available parallel corpo…

cs.CL2021★ 3 cited

Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

Benjamin Marie, Atsushi Fujita, Raphael Rubino

This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our…

cs.CL2021

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…