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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 1601–1650 of 10752 papers

TitleStatusHype
Beyond the C: Retargetable Decompilation using Neural Machine Translation—0
Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation—0
Controlling Styles in Neural Machine Translation with Activation PromptCode1
AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation—0
A Simple Baseline for Beam Search Reranking—0
Convolution-enhanced Evolving Attention NetworksCode1
Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better—0
Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models—0
Multi-VALUE: A Framework for Cross-Dialectal English NLP—0
Attention as a Guide for Simultaneous Speech TranslationCode0
Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation—0
Causes and Cures for Interference in Multilingual Translation—0
Towards a general purpose machine translation system for Sranantongo—0
Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata—0
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning—0
Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators—0
P-Transformer: Towards Better Document-to-Document Neural Machine TranslationCode0
End-to-End Speech Translation of Arabic to English Broadcast News—0
Spatio-temporal Sign Language Representation and Translation—0
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine TranslationCode1
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding—0
M3ST: Mix at Three Levels for Speech Translation—0
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation—0
Neural Machine Translation with Contrastive Translation MemoriesCode1
In-context Examples Selection for Machine TranslationCode0
Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical DomainCode0
Building Metadata Inference Using a Transducer Based Language Model—0
Democratizing Neural Machine Translation with OPUS-MTCode2
The RoyalFlush System for the WMT 2022 Efficiency Task—0
Subword-Delimited Downsampling for Better Character-Level TranslationCode0
Improving Simultaneous Machine Translation with Monolingual DataCode1
Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22Code0
Deep neural network techniques for monaural speech enhancement: state of the art analysis—0
CUNI Systems for the WMT22 Czech-Ukrainian Translation Task—0
Long-Document Cross-Lingual SummarizationCode0
Word Alignment in the Era of Deep Learning: A Tutorial—0
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing—0
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think—0
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning—0
Rephrasing the Reference for Non-Autoregressive Machine Translation—0
Findings of the WMT 2022 Shared Task on Translation Suggestion—0
PhraseTransformer: An Incorporation of Local Context Information into Sequence-to-sequence Semantic ParsingCode0
Soft Alignment Objectives for Robust Adaptation of Language GenerationCode0
Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in GondiCode1
Extending the Subwording Model of Multilingual Pretrained Models for New LanguagesCode0
Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task—0
Considerations for meaningful sign language machine translation based on glosses—0
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?Code0
TorchScale: Transformers at Scale—0
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence ModelingCode1
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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