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 2151–2200 of 10752 papers

TitleStatusHype
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse LanguagesCode0
Lack of Fluency is Hurting Your Translation Model—0
Principled Paraphrase Generation with Parallel CorporaCode0
The Importance of Being Parameters: An Intra-Distillation Method for Serious GainsCode1
Summarize and Generate to Back-translate: Unsupervised Translation of Programming LanguagesCode1
Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the TransformerCode1
When does Parameter-Efficient Transfer Learning Work for Machine Translation?Code0
A Template-based Method for Constrained Neural Machine TranslationCode1
Local Byte Fusion for Neural Machine TranslationCode0
Multilingual Machine Translation with Hyper-AdaptersCode0
How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contextsCode0
What Do Compressed Multilingual Machine Translation Models Forget?Code0
Non-Autoregressive Neural Machine Translation: A Call for Clarity—0
Transition-based Semantic Role Labeling with Pointer NetworksCode0
Lossless Acceleration for Seq2seq Generation with Aggressive DecodingCode0
Understanding and Mitigating the Uncertainty in Zero-Shot Translation—0
SALTED: A Framework for SAlient Long-Tail Translation Error Detection—0
Translating Hanja Historical Documents to Contemporary Korean and English—0
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities—0
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters—0
Twist Decoding: Diverse Generators Guide Each OtherCode1
PreQuEL: Quality Estimation of Machine Translation Outputs in AdvanceCode0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Exploiting Social Media Content for Self-Supervised Style TransferCode0
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval—0
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine TranslationCode0
Consistent Human Evaluation of Machine Translation across Language Pairs—0
Directed Acyclic Transformer for Non-Autoregressive Machine TranslationCode1
Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks—0
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust—0
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation—0
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data—0
Mitigating Gender Stereotypes in Hindi and Marathi—0
Identifying concept libraries from language about object structureCode1
ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair—0
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation—0
Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation—0
Should attention be all we need? The epistemic and ethical implications of unification in machine learning—0
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to FormalityCode1
Building Machine Translation Systems for the Next Thousand Languages—0
Sub-Word Alignment Is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine TranslationCode0
Vector Representations of Idioms in Conversational Systems—0
Bridging the Domain Gap for Stance Detection for the Zulu language—0
Example-Based Machine Translation from Text to a Hierarchical Representation of Sign Language—0
Quantifying Synthesis and Fusion and their Impact on Machine Translation—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation PerformanceCode0
Non-Autoregressive Machine Translation: It's Not as Fast as it Seems—0
Training Mixed-Domain Translation Models via Federated Learning—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