SOTAVerified

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 5651–5700 of 10752 papers

TitleStatusHype
What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP ModelsCode1
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks—0
How Much Does Tokenization Affect Neural Machine Translation?—0
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between ModalitiesCode0
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks—0
DTMT: A Novel Deep Transition Architecture for Neural Machine TranslationCode0
Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation—0
TextBugger: Generating Adversarial Text Against Real-world ApplicationsCode0
SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences—0
Sentence-wise Smooth Regularization for Sequence to Sequence Learning—0
Machine Translation : From Statistical to modern Deep-learning practices—0
Conditional Variational Autoencoder for Neural Machine Translation—0
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous OutputsCode0
Bayesian Layers: A Module for Neural Network Uncertainty—0
The USTC-NEL Speech Translation system at IWSLT 2018—0
Embedding-reparameterization procedure for manifold-valued latent variables in generative modelsCode0
Adpositional Supersenses for Mandarin Chinese—0
Verification of deep probabilistic models—0
Neural Abstractive Text Summarization with Sequence-to-Sequence ModelsCode1
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling—0
The RGNLP Machine Translation Systems for WAT 2018—0
Statistical Machine Translation Using 5-grams Word Segmentation in Decoding—0
Combination of Statistical and Neural Machine Translation for Myanmar-English—0
SRCB Neural Machine Translation Systems in WAT 2018—0
English-Myanmar NMT and SMT with Pre-ordering: NICT’s Machine Translation Systems at WAT-2018—0
TMU Japanese-English Neural Machine Translation System using Generative Adversarial Network for WAT 2018—0
UCSYNLP-Lab Machine Translation Systems for WAT 2018—0
Automatic Error Correction on Japanese Functional Expressions Using Character-based Neural Machine Translation—0
XMU Neural Machine Translation Systems for WAT2018 Myanmar-English Translation Task—0
IITP-MT at WAT2018: Transformer-based Multilingual Indic-English Neural Machine Translation System—0
Automatic Evaluation of English-to-Korean and Korean-to-English Neural Machine Translation Systems by Linguistic Test Points—0
Multi-dialect Neural Machine Translation and Dialectometry—0
Chinese Spelling Check based on Neural Machine Translation—0
Unsupervised Mining of Analogical Frames by Constraint SatisfactionCode0
Development of Natural Language Processing Tools for Cook Islands M\=aori—0
Towards Efficient Machine Translation Evaluation by Modelling Annotators—0
Improved Neural Machine Translation using Side Information—0
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation—0
Unsupervised Multi-modal Neural Machine Translation—0
Stochastic Gradient Push for Distributed Deep LearningCode0
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems—0
Sentence Encoding with Tree-constrained Relation Networks—0
Connecting the Dots Between MLE and RL for Sequence Prediction—0
A Hierarchical Neural Network for Sequence-to-Sequences Learning—0
Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languagesCode0
Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma PredictionCode0
Neural Machine Translation with Adequacy-Oriented Learning—0
Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical RepresentationsCode0
GPipe: Efficient Training of Giant Neural Networks using Pipeline ParallelismCode2
Modeling Coherence for Discourse Neural Machine Translation—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