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 8951–9000 of 10752 papers

TitleStatusHype
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
On the practice of error analysis for machine translation evaluation—0
On the Properties of Neural Machine Translation: Encoder--Decoder Approaches—0
On the Relation between Position Information and Sentence Length in Neural Machine Translation—0
On the Relationship between Neural Machine Translation and Word Alignment—0
On the Robustness of Self-Attentive Models—0
On the Role of Parallel Data in Cross-lingual Transfer Learning—0
On the Same Page? Comparing Inter-Annotator Agreement in Sentence and Document Level Human Machine Translation Evaluation—0
On the Sequence Evaluation based on Stochastic Processes—0
On the Shortcut Learning in Multilingual Neural Machine Translation—0
On the Sparsity of Neural Machine Translation Models—0
On the String Translations Produced by Multi Bottom--Up Tree Transducers—0
On the Sub-Layer Functionalities of Transformer Decoder—0
On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages—0
On the use of BERT for Neural Machine Translation—0
On the use of linguistic similarities to improve Neural Machine Translation for African Languages—0
On the Use of Machine Translation-Based Approaches for Vietnamese Diacritic Restoration—0
On the Use of Metaphor Translation in Psychiatry—0
On the Utility of Lay Summaries and AI Safety Disclosures: Toward Robust, Open Research Oversight—0
On the Vector Representation of Utterances in Dialogue Context—0
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models—0
On the Weaknesses of Reinforcement Learning for Neural Machine Translation—0
On the Word Alignment from Neural Machine Translation—0
Ontology-based Technical Text Annotation—0
Ontology Label Translation—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
On Translating Technical Terminology: A Translation Workflow for Machine-Translated Acronyms—0
ONTS: "Optima" News Translation System—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