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 6551–6600 of 10752 papers

TitleStatusHype
Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation—0
Recurrent Neural Network based Translation Quality Estimation—0
Recurrent Neural Network-based Tuple Sequence Model for Machine Translation—0
Recurrent-Neural-Network for Language Detection on Twitter Code-Switching Corpus—0
Recurrent Neural Networks for Word Alignment Model—0
Recurrent Positional Embedding for Neural Machine Translation—0
Recurrent Stacking of Layers for Compact Neural Machine Translation Models—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Recursive Autoencoders for ITG-Based Translation—0
Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation—0
Recycling a Pre-trained BERT Encoder for Neural Machine Translation—0
RED: A Reference Dependency Based MT Evaluation Metric—0
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis—0
Re-design of the Machine Translation Training Tool (MT3)—0
RED, The DCU-CASICT Submission of Metrics Tasks—0
Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information—0
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation—0
Reducing Hallucinations in Neural Machine Translation with Feature Attribution—0
Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method—0
Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework—0
Reducing the Impact of Data Sparsity in Statistical Machine Translation—0
Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queries—0
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach—0
Redundancy Detection in ESL Writings—0
Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE—0
Re-examining Machine Translation Metrics for Paraphrase Identification—0
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems—0
Reference Bias in Monolingual Machine Translation Evaluation—0
Reference Network for Neural Machine Translation—0
Referential Cohesion A Challenge for Machine Translation Evaluation—0
Referential Translation Machines for Predicting Translation Quality and Related Statistics—0
Referential Translation Machines for Predicting Translation Performance—0
Referential Translation Machines for Predicting Translation Quality—0
Referential Translation Machines for Quality Estimation—0
Refinements to Interactive Translation Prediction Based on Search Graphs—0
Refining an Almost Clean Translation Memory Helps Machine Translation—0
Refining Source Representations with Relation Networks for Neural Machine Translation—0
Refining Source Representations with Relation Networks for Neural Machine Translation—0
Refining the state-of-the-art in Machine Translation, optimizing NMT for the JA <-> EN language pair by leveraging personal domain expertise—0
Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach—0
Refining Word Segmentation Using a Manually Aligned Corpus for Statistical Machine Translation—0
Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation—0
Regression with Phrase Indicators for Estimating MT Quality—0
Regularization techniques for fine-tuning in neural machine translation—0
Regularized Context Gates on Transformer for Machine Translation—0
Regularized Interlingual Projections: Evaluation on Multilingual Transliteration—0
Regularized Minimum Error Rate Training—0
Regularizing Neural Machine Translation by Target-bidirectional Agreement—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