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 3451–3500 of 10752 papers

TitleStatusHype
An Incremental Algorithm for Transition-based CCG Parsing—0
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation—0
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data—0
Entropy-based Pruning for Phrase-based Machine Translation—0
Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation—0
Entropy-based Training Data Selection for Domain Adaptation—0
EPA: Easy Prompt Augmentation on Large Language Models via Multiple Sources and Multiple Targets—0
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation—0
Bilingual Embeddings with Random Walks over Multilingual Wordnets—0
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation—0
Bilingual Embeddings and Word Alignments for Translation Quality Estimation—0
An In-Context and Collaborative Software Localisation Model—0
Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
Bilingual Dictionary Construction with Transliteration Filtering—0
Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models—0
Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese—0
Error Annotation in Post-Editing Machine Translation: Investigating the Impact of Text-to-Speech Technology—0
Error Identification for Machine Translation with Metric Embedding and Attention—0
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models—0
An Incomplete Loop: Deductive, Inductive, and Abductive Learning in Large Language Models—0
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?—0
ERUPD -- English to Roman Urdu Parallel Dataset—0
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization—0
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation—0
"Es geht um Respekt, nicht um Technologie": Erkenntnisse aus einem Interessensgruppen-übergreifenden Workshop zu genderfairer Sprache und Sprachtechnologie—0
ESPnet-ST: All-in-One Speech Translation Toolkit—0
Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation—0
An inclusive review on deep learning techniques and their scope in handwriting recognition—0
Establishing sentential structure via realignments from small parallel corpora—0
Estimating Grammar Correctness for a Priori Estimation of Machine Translation Post-Editing Effort—0
Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models—0
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages—0
Estimating the Quality of Translated User-Generated Content—0
Estimating Word Alignment Quality for SMT Reordering Tasks—0
Estimation vs Metrics: is QE Useful for MT Model Selection?—0
A Corpus of Spontaneous Speech in Lectures: The KIT Lecture Corpus for Spoken Language Processing and Translation—0
Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers—0
Domain Terminology Integration into Machine Translation: Leveraging Large Language Models—0
EthioMT: Parallel Corpus for Low-resource Ethiopian Languages—0
Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis—0
\'Etude de l'apprentissage par transfert de syst\`emes de traduction automatique neuronaux (Study on transfer learning in neural machine translation )—0
Etude de l'impact d'un lexique bilingue sp\'ecialis\'e sur la performance d'un moteur de traduction \`a base d'exemples (Studying the impact of a specialized bilingual lexicon on the performance of an example-based machine translation engine)—0
EU-BRIDGE MT: Combined Machine Translation—0
EuroLLM-9B: Technical Report—0
EuroLLM: Multilingual Language Models for Europe—0
Euronews: a multilingual speech corpus for ASR—0
Europarl: A Parallel Corpus for Statistical Machine Translation—0
Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates—0
Bilingual Data Cleaning for SMT using Graph-based Random Walk—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
5AdminBLEU score43.8—Unverified
6Transformer (ADMIN init)BLEU 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