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Found 39 papers, 5 papers with code
NICT Kyoto Submission for the WMT’21 Quality Estimation Task: Multimetric Multilingual Pretraining for Critical Error Detection
This paper presents the NICT Kyoto submission for the WMT’21 Quality Estimation (QE) Critical Error Detection shared task (Task 3).
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models
This paper investigates a combination of SD and TL for training efficient NMT models for ELR settings, where we utilize TL with helping corpora twice: once for distilling the ELR corpora and then during compact model training.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
+4
Combination of Neural Machine Translation Systems at WMT20
This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese->English translation tasks.
Investigating Softmax Tempering for Training Neural Machine Translation Models
In low-resource scenarios and NMT models tend to perform poorly because the model training quickly converges to a point where the softmax distribution computed using logits approaches the gold label distribution.
Attainable Text-to-Text Machine Translation vs. Translation: Issues Beyond Linguistic Processing
Existing approaches for machine translation (MT) mostly translate given text in the source language into the target language and without explicitly referring to information indispensable for producing proper translation.
Unsupervised Translation Quality Estimation Exploiting Synthetic Data and Pre-trained Multilingual Encoder
In this paper, we extensively investigate the usefulness of synthetic TQE data and pre-trained multilingual encoders in unsupervised sentence-level TQE, both of which have been proven effective in the supervised training scenarios.
Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts
To create the parallel corpora, we propose a dynamic programming based sentence alignment algorithm which leverages the cosine similarity of machine-translated sentences.
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers
MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable.
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation
Finally, we analyze the effects of recurrently stacked layers by visualizing the attentions of models that use recurrently stacked layers and models that do not.
Understanding Pre-Editing for Black-Box Neural Machine Translation
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.
Synthesizing Monolingual Data for Neural Machine Translation
Nonetheless, large monolingual data in the target domains or languages are not always available to generate large synthetic parallel data.
Softmax Tempering for Training Neural Machine Translation Models
Neural machine translation (NMT) models are typically trained using a softmax cross-entropy loss where the softmax distribution is compared against smoothed gold labels.
Tagged Back-translation Revisited: Why Does It Really Work?
In this paper, we show that neural machine translation (NMT) systems trained on large back-translated data overfit some of the characteristics of machine-translated texts.
Balancing Cost and Benefit with Tied-Multi Transformers
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 number of encoder and decoder layers during decoding.
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera.
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English
This paper presents the NICT{'}s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks.
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation
This paper highlights the impressive utility of multi-parallel corpora for transfer learning in a one-to-many low-resource neural machine translation (NMT) setting.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
+3
Multi-Layer Softmaxing during Training Neural Machine Translation for Flexible Decoding with Fewer Layers
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 the number of encoder and decoder layers for decoding.
NICT's Machine Translation Systems for the WMT19 Similar Language Translation Task
Our primary submission to the task is the result of a simple combination of our SMT and NMT systems.
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔English, Gujarati↔English, Chinese↔English, and English→Finnish translation directions.
NICT's Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task
This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task.
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking.
Unsupervised Joint Training of Bilingual Word Embeddings
State-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space.
Unsupervised Extraction of Partial Translations for Neural Machine Translation
We propose a new algorithm for extracting from monolingual data what we call partial translations: pairs of source and target sentences that contain sequences of tokens that are translations of each other.
Unsupervised Neural Machine Translation Initialized by Unsupervised Statistical Machine Translation
In this work, we propose to define unsupervised NMT (UNMT) as NMT trained with the supervision of synthetic bilingual data.
NICT's Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task
Our systems are ranked first for the Estonian-English and Finnish-English language pairs (constraint) according to BLEU-cased.
Recurrent Stacking of Layers for Compact Neural Machine Translation Models
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.
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification
This paper examines the usefulness of semantic features based on word alignments for estimating the quality of text simplification.
Japanese to English/Chinese/Korean Datasets for Translation Quality Estimation and Automatic Post-Editing
Aiming at facilitating the research on quality estimation (QE) and automatic post-editing (APE) of machine translation (MT) outputs, especially for those among Asian languages, we have created new datasets for Japanese to English, Chinese, and Korean translations.
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings
We propose a new method for extracting pseudo-parallel sentences from a pair of large monolingual corpora, without relying on any document-level information.
Consistent Classification of Translation Revisions: A Case Study of English-Japanese Student Translations
This paper also describes an application of our scheme to an English-to-Japanese translation exercise course for undergraduate students at a university in Japan.
Phrase Table Induction Using In-Domain Monolingual Data for Domain Adaptation in Statistical Machine Translation
We present a new framework to induce an in-domain phrase table from in-domain monolingual data that can be used to adapt a general-domain statistical machine translation system to the targeted domain.
