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 7751–7800 of 10752 papers

TitleStatusHype
Learning variable length units for SMT between related languages via Byte Pair Encoding—0
Learning When to Attend for Neural Machine Translation—0
Learning with Noise-Contrastive Estimation: Easing training by learning to scale—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
Learning Word Meta-Embeddings—0
Learning Word Reorderings for Hierarchical Phrase-based Statistical Machine Translation—0
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation—0
Learn to Talk via Proactive Knowledge Transfer—0
Leave-One-Out Phrase Model Training for Large-Scale Deployment—0
Leave-one-out Word Alignment without Garbage Collector Effects—0
LeBLEU: N-gram-based Translation Evaluation Score for Morphologically Complex Languages—0
Lecture Translator - Speech translation framework for simultaneous lecture translation—0
Left-to-Right Tree-to-String Decoding with Prediction—0
LegoNN: Building Modular Encoder-Decoder Models—0
Length-Incremental Phrase Training for SMT—0
Lenient Evaluation of Japanese Speech Recognition: Modeling Naturally Occurring Spelling Inconsistency—0
Le Petit Prince in UNL—0
LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors—0
Lesan -- Machine Translation for Low Resource Languages—0
Les lettres et la machine : un état de l’art en traduction littéraire automatique (Machines in the humanities: current state of the art in literary machine translation)—0
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data—0
Let Sense Bags Do Talking: Cross Lingual Word Semantic Similarity for English and Hindi—0
LetsMT!: Cloud-Based Platform for Do-It-Yourself Machine Translation—0
Letter N-Gram-based Input Encoding for Continuous Space Language Models—0
Letter Sequence Labeling for Compound Splitting—0
Letz Translate: Low-Resource Machine Translation for Luxembourgish—0
Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations—0
Leveraging Automated Unit Tests for Unsupervised Code Translation—0
Leveraging backtranslation to improve machine translation for Gaelic languages—0
Leveraging Compounds to Improve Noun Phrase Translation from Chinese and German—0
Leveraging Crowdsourcing for Paraphrase Recognition—0
Leveraging Data Resources for Cross-Linguistic Information Retrieval Using Statistical Machine Translation—0
Leveraging Discourse Rewards for Document-Level Neural Machine Translation—0
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations—0
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation—0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration—0
Leveraging GPT-4 for Automatic Translation Post-Editing—0
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis—0
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora—0
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation—0
Leveraging Multilingual Training for Limited Resource Event Extraction—0
Leveraging Orthographic Similarity for Multilingual Neural Transliteration—0
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade—0
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling—0
Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models—0
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting—0
Leveraging Small Multilingual Corpora for SMT Using Many Pivot Languages—0
Leveraging SNOMED CT terms and relations for machine translation of clinical texts from Basque to Spanish—0
Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition—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