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Found 28 papers, 2 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).
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.
Preliminary WMT24 Ranking of General MT Systems and LLMs
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics.
An Automatic Evaluation of the WMT22 General Machine Translation Task
This report presents an automatic evaluation of the general machine translation task of the Seventh Conference on Machine Translation (WMT22).
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.
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.
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.
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.
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.
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.
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.
NICT's Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task
Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data.
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.
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.
