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 3501–3550 of 10752 papers

TitleStatusHype
European Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management—0
EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text—0
EvalD Reference-Less Discourse Evaluation for WMT18—0
Evaluating a Machine Translation System in a Technical Support Scenario—0
Evaluating Amharic Machine Translation—0
Evaluating and Combining Name Entity Recognition Systems—0
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis—0
Evaluating (and Improving) Sentence Alignment under Noisy Conditions—0
Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models—0
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark—0
Evaluating Appropriateness Of System Responses In A Spoken CALL Game—0
Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese—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
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation—0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization—0
Evaluating Discourse Phenomena in Neural Machine Translation—0
Evaluating Domain Adaptation for Machine Translation Across Scenarios—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
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages—0
A Corpus of Spontaneous Speech in Lectures: The KIT Lecture Corpus for Spoken Language Processing and Translation—0
Domain Terminology Integration into Machine Translation: Leveraging Large Language Models—0
Evaluating Gender Bias in the Translation of Gender-Neutral Languages into English—0
Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis—0
Evaluating Grammaticality in Seq2seq Models with a Broad Coverage HPSG Grammar: A Case Study on Machine Translation—0
Evaluating Improvised Hip Hop Lyrics - Challenges and Observations—0
Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine Translation—0
Bilingual Data Cleaning for SMT using Graph-based Random Walk—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
Evaluating machine translation for assimilation via a gap-filling task—0
Evaluating machine translation in a low-resource language combination: Spanish-Galician.—0
Evaluating Machine Translation in a Usage Scenario—0
Evaluating Machine Translation in Cross-lingual E-Commerce Search—0
Domain Specific Sub-network for Multi-Domain Neural Machine Translation—0
Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation—0
An Improvement in Cross-Language Document Retrieval Based on Statistical Models—0
Evaluating Machine Translation Systems with Second Language Proficiency Tests—0
Evaluating MT Systems: A Theoretical Framework—0
Domain-Specific Paraphrase Extraction—0
Evaluating Neural Machine Translation in English-Japanese Task—0
Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis—0
Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks—0
Domain-specific MT for Low-resource Languages: The case of Bambara-French—0
Domain-Specific Machine Translation to Translate Medicine Brochures in English to Sorani Kurdish—0
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences—0
Evaluating Robustness to Input Perturbations for Neural Machine Translation—0
An Improved Hierarchical Word Sequence Language Model Using Directional Information—0
Domain specialization: a post-training domain adaptation for Neural Machine Translation—0
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation—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