29 citations · 63 across the 16 of their papers we have counts for
16 papers
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…
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…
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…
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…
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…
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…
Understanding Pre-Editing for Black-Box Neural Machine Translation
Rei Miyata, Atsushi Fujita
Pre-editing is the process of modifying the source text (ST) so that it can be translated by machine translation (MT) in a better quality. Despite the unpredictability of black-box…
Synthesizing Monolingual Data for Neural Machine Translation
Benjamin Marie, Atsushi Fujita
In neural machine translation (NMT), monolingual data in the target language are usually exploited through a method so-called "back-translation" to synthesize additional training p…
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…
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…
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…
Multi-Layer Softmaxing during Training Neural Machine Translation for Flexible Decoding with Fewer Layers
Raj Dabre, Atsushi Fujita
This paper proposes a novel procedure for training an encoder-decoder based deep neural network which compresses NxM models into a single model enabling us to dynamically choose th…
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
Aizhan Imankulova, Raj Dabre, Atsushi Fujita +1
This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmar…
Unsupervised Neural Machine Translation Initialized by Unsupervised Statistical Machine Translation
Benjamin Marie, Atsushi Fujita
Recent work achieved remarkable results in training neural machine translation (NMT) systems in a fully unsupervised way, with new and dedicated architectures that rely on monoling…
NICT's Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task
Benjamin Marie, Rui Wang, Atsushi Fujita +2
This paper presents the NICT's participation to the WMT18 shared news translation task. We participated in the eight translation directions of four language pairs: Estonian-English…
Recurrent Stacking of Layers for Compact Neural Machine Translation Models
Raj Dabre, Atsushi Fujita
In neural machine translation (NMT), the most common practice is to stack a number of recurrent or feed-forward layers in the encoder and the decoder. As a result, the addition of…