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 5901–5950 of 10752 papers

TitleStatusHype
Online Learning over Time in Adaptive Neural Machine Translation—0
Online optimisation of log-linear weights in interactive machine translation—0
Online Polylingual Topic Models for Fast Document Translation Detection—0
Online Relative Margin Maximization for Statistical Machine Translation—0
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue—0
Online Word Alignment for Online Adaptive Machine Translation—0
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation—0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation—0
On Machine Translation of User Reviews—0
On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models—0
On NMT Search Errors and Model Errors: Cat Got Your Tongue?—0
On Orthogonality Constraints for Transformers—0
On Randomized Classification Layers and Their Implications in Natural Language Generation—0
On Romanization for Model Transfer Between Scripts in Neural Machine Translation—0
On Search Strategies for Document-Level Neural Machine Translation—0
On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi—0
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models—0
On Statistical Machine Translation and Translation Theory—0
On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation—0
On Target Representation in Continuous-output Neural Machine Translation—0
On Target Segmentation for Direct Speech Translation—0
On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation—0
On the annotation of TMX translation memories for advanced leveraging in computer-aided translation—0
On the ``Calligraphy'' of Books—0
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation—0
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation—0
On the Depth between Beam Search and Exhaustive Search for Text Generation—0
On the differences between BERT and MT encoder spaces and how to address them in translation tasks—0
On the differences between human translations—0
On the Discrepancy between Density Estimation and Sequence Generation—0
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On the Elements of an Accurate Tree-to-String Machine Translation System—0
On the Evaluation of Machine Translation n-best Lists—0
On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?—0
On the Explainability of Natural Language Processing Deep Models—0
On the Exploration of English to Urdu Machine Translation—0
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures—0
On-the-Fly Fusion of Large Language Models and Machine Translation—0
On the Implications of Verbose LLM Outputs: A Case Study in Translation Evaluation—0
On the Influence of Machine Translation on Language Origin Obfuscation—0
On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation—0
Towards Inducing Document-Level Abilities in Standard Multilingual Neural Machine Translation Models—0
On the Language Coverage Bias for Neural Machine Translation—0
On the Limitations of Reference-Free Evaluations of Generated Text—0
On the Limitations of Unsupervised Bilingual Dictionary Induction—0
On the Linguistic Representational Power of Neural Machine Translation Models—0
On the origin of errors: A fine-grained analysis of MT and PE errors and their relationship—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