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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10251–10300 of 10752 papers

TitleStatusHype
UOW: Semantically Informed Text Similarity—0
UParse: the Edinburgh system for the CoNLL 2017 UD shared task—0
UPC-CORE: What Can Machine Translation Evaluation Metrics and Wikipedia Do for Estimating Semantic Textual Similarity?—0
UPF-Cobalt Submission to WMT15 Metrics Task—0
UPM system for WMT 2012—0
The IIT Bombay English-Hindi Parallel Corpus—0
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?—0
UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension—0
Urdu-English Machine Transliteration using Neural Networks—0
Urdu Hindi Machine Transliteration using SMT—0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
Urdu Spell Checking: Reverse Edit Distance Approach—0
Urdu To Punjabi Machine Translation System—0
USAAR: An Operation Sequential Model for Automatic Statistical Post-Editing—0
USAAR-DFKI -- The Transference Architecture for English--German Automatic Post-Editing—0
USAAR-SAPE: An English--Spanish Statistical Automatic Post-Editing System—0
USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics—0
The Joy of Parallelism with CzEng 1.0—0
Usefulness of Emotional Prosody in Neural Machine Translation—0
Use of Domain-Specific Language Resources in Machine Translation—0
The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education—0
Use of Modality and Negation in Semantically-Informed Syntactic MT—0
User expectations towards machine translation: A case study—0
Users and Data: The Two Neglected Children of Bilingual Natural Language Processing Research—0
USFD's Phrase-level Quality Estimation Systems—0
USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box—0
USHEF and USAAR-USHEF participation in the WMT15 QE shared task—0
Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems—0
Using a Graph-based Coherence Model in Document-Level Machine Translation—0
Using Ambiguity Detection to Streamline Linguistic Annotation—0
Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora—0
Using a Random Forest Classifier to recognise translations of biomedical terms across languages—0
Using a Serious Game to Collect a Child Learner Speech Corpus—0
Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination—0
Using BabelNet to Improve OOV Coverage in SMT—0
Using Bilingual Patents for Translation Training—0
Using Bilingual Segments in Generating Word-to-word Translations—0
Using bilingual word-embeddings for multilingual collocation extraction—0
Using Categorial Grammar to Label Translation Rules—0
Using character overlap to improve language transformation—0
Using CollGram to Compare Formulaic Language in Human and Neural Machine Translation—0
Using CollGram to Compare Formulaic Language in Human and Machine Translation—0
Using Comparable Corpora to Adapt MT Models to New Domains—0
Zero-Shot Neural Machine Translation with Self-Learning Cycle—0
Using Context in Neural Machine Translation Training Objectives—0
Using Contextual Information for Machine Translation Evaluation—0
Using Context Vectors in Improving a Machine Translation System with Bridge Language—0
XDLM: Cross-lingual Diffusion Language Model for Machine Translation—0
Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation—0
Using Crowd Agreement for Wordnet Localization—0
Show:102550
← PrevPage 206 of 216Next →
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