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Machine Translation

Machine translation is the task of translating a sentence in a source language to a different target language.

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

( Image credit: Google seq2seq )

Papers

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Showing 5851–5900 of 10752 papers

TitleStatusHype
EvalD Reference-Less Discourse Evaluation for WMT18—0
Ensemble of Translators with Automatic Selection of the Best Translation -- the submission of FOKUS to the WMT 18 biomedical translation task --—0
An Unsupervised System for Parallel Corpus Filtering—0
Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper—0
TencentFmRD Neural Machine Translation for WMT18—0
Tencent Neural Machine Translation Systems for WMT18—0
Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs—0
The Word Sense Disambiguation Test Suite at WMT18—0
The JHU Parallel Corpus Filtering Systems for WMT 2018—0
The MLLP-UPV German-English Machine Translation System for WMT18—0
Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings—0
Discourse-Related Language Contrasts in English-Croatian Human and Machine Translation—0
LMU Munich's Neural Machine Translation Systems at WMT 2018—0
Tilde's Machine Translation Systems for WMT 2018Code0
The University of Edinburgh's Submissions to the WMT18 News Translation Task—0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task—0
Robust parfda Statistical Machine Translation Results—0
Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings—0
An Empirical Study of Machine Translation for the Shared Task of WMT18—0
STACC, OOV Density and N-gram Saturation: Vicomtech's Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Sheffield Submissions for WMT18 Multimodal Translation Shared Task—0
RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation EvaluationCode0
Multi-source transformer with combined losses for automatic post editing—0
Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech's Participation in the WMT 2018 Quality Estimation Task—0
The NiuTrans Machine Translation System for WMT18—0
PROMT Systems for WMT 2018 Shared Translation Task—0
The University of Maryland's Chinese-English Neural Machine Translation Systems at WMT18—0
The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018—0
Keep It or Not: Word Level Quality Estimation for Post-Editing—0
Neural Machine Translation for English-TamilCode0
Proceedings of the Third Conference on Machine Translation: Research Papers—0
Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task—0
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task—0
ITER: Improving Translation Edit Rate through Optimizable Edit Costs—0
The LMU Munich Unsupervised Machine Translation Systems—0
Automatically Tailoring Unsupervised Morphological Segmentation to the Language—0
CUNI Transformer Neural MT System for WMT18—0
CUNI System for the WMT18 Multimodal Translation Task—0
Proceedings of the Third Conference on Machine Translation: Shared Task Papers—0
CUNI Submissions in WMT18—0
RTM results for Predicting Translation Performance—0
Input Combination Strategies for Multi-Source Transformer Decoder—0
Phonological Features for Morphological Inflection—0
The Benefit of Pseudo-Reference Translations in Quality Estimation of MT Output—0
Multi-encoder Transformer Network for Automatic Post-Editing—0
UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—0
Coverage and Cynicism: The AFRL Submission to the WMT 2018 Parallel Corpus Filtering Task—0
Tilde's Parallel Corpus Filtering Methods for WMT 2018—0
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All datasetsWMT2014 English-GermanWMT2014 English-FrenchIWSLT2014 German-EnglishACESWMT2016 English-RomanianWMT2016 Romanian-EnglishWMT2014 German-EnglishIWSLT2015 German-EnglishWMT2016 English-GermanIWSLT2015 English-VietnameseIWSLT2015 English-GermanWMT2016 German-English

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer Cycle (Rev)BLEU score35.14—Unverified
2Noisy back-translationBLEU score35—Unverified
3Transformer+Rep(Uni)BLEU score33.89—Unverified
4T5-11BBLEU score32.1—Unverified
5BiBERTBLEU score31.26—Unverified
6Transformer + R-DropBLEU score30.91—Unverified
7Bi-SimCutBLEU score30.78—Unverified
8BERT-fused NMTBLEU score30.75—Unverified
9Data Diversification - TransformerBLEU score30.7—Unverified
10SimCutBLEU score30.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Transformer+BT (ADMIN init)BLEU score46.4—Unverified
2Noisy back-translationBLEU score45.6—Unverified
3mRASP+Fine-TuneBLEU score44.3—Unverified
4Transformer + R-DropBLEU score43.95—Unverified
5Transformer (ADMIN init)BLEU score43.8—Unverified
6AdminBLEU score43.8—Unverified
7BERT-fused NMTBLEU score43.78—Unverified
8MUSE(Paralllel Multi-scale Attention)BLEU score43.5—Unverified
9T5BLEU score43.4—Unverified
10Local Joint Self-attentionBLEU score43.3—Unverified
#ModelMetricClaimedVerifiedStatus
1PiNMTBLEU score40.43—Unverified
2BiBERTBLEU score38.61—Unverified
3Bi-SimCutBLEU score38.37—Unverified
4Cutoff + Relaxed Attention + LMBLEU score37.96—Unverified
5DRDABLEU score37.95—Unverified
6Transformer + R-Drop + CutoffBLEU score37.9—Unverified
7SimCutBLEU score37.81—Unverified
8Cutoff+KneeBLEU score37.78—Unverified
9CutoffBLEU score37.6—Unverified
10CipherDAugBLEU score37.53—Unverified
#ModelMetricClaimedVerifiedStatus
1HWTSC-Teacher-SimScore19.97—Unverified
2MS-COMET-22Score19.89—Unverified
3MS-COMET-QE-22Score19.76—Unverified
4KG-BERTScoreScore17.28—Unverified
5metricx_xl_DA_2019Score17.17—Unverified
6COMET-QEScore16.8—Unverified
7COMET-22Score16.31—Unverified
8UniTE-srcScore15.68—Unverified
9UniTE-refScore15.38—Unverified
10metricx_xxl_DA_2019Score15.24—Unverified