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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 8401–8450 of 10752 papers

TitleStatusHype
Multi-level Evaluation for Machine Translation—0
MT Tuning on RED: A Dependency-Based Evaluation Metric—0
The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015—0
Edinburgh's Syntax-Based Systems at WMT 2015—0
Morphological Segmentation and OPUS for Finnish-English Machine Translation—0
Montreal Neural Machine Translation Systems for WMT'15—0
An Investigation of Machine Translation Evaluation Metrics in Cross-lingual Question Answering—0
Drem: The AFRL Submission to the WMT15 Tuning Task—0
Machine Translation Evaluation using Recurrent Neural NetworksCode0
Strategy-Based Technology for Estimating MT Quality—0
UAlacant word-level machine translation quality estimation system at WMT 2015—0
LORIA System for the WMT15 Quality Estimation Shared Task—0
UdS-Sant: English--German Hybrid Machine Translation System—0
Discontinuous Statistical Machine Translation with Target-Side Dependency Syntax—0
ListNet-based MT Rescoring—0
BEER 1.1: ILLC UvA submission to metrics and tuning taskCode0
LIMSI@WMT'15 : Translation Task—0
DFKI's experimental hybrid MT system for WMT 2015—0
Abu-MaTran at WMT 2015 Translation Task: Morphological Segmentation and Web Crawling—0
LeBLEU: N-gram-based Translation Evaluation Score for Morphologically Complex Languages—0
Sheffield Systems for the Finnish-English WMT Translation Task—0
Dependency Analysis of Scrambled References for Better Evaluation of Japanese Translation—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
The KIT-LIMSI Translation System for WMT 2015—0
Data Selection With Fewer Words—0
Data enhancement and selection strategies for the word-level Quality Estimation—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2015—0
Investigations on Phrase-based Decoding with Recurrent Neural Network Language and Translation Models—0
The University of Illinois submission to the WMT 2015 Shared Translation Task—0
CUNI in WMT15: Chimera Strikes Again—0
VERTa: a Linguistically-motivated Metric at the WMT15 Metrics Task—0
Improving evaluation and optimization of MT systems against MEANT—0
USHEF and USAAR-USHEF participation in the WMT15 QE shared task—0
How do Humans Evaluate Machine Translation—0
Hierarchical Machine Translation With Discontinuous Phrases—0
Connotation in Translation—0
The AFRL-MITLL WMT15 System: There's More than One Way to Decode It!—0
GF Wide-coverage English-Finnish MT system for WMT 2015—0
Semantic Tuples for Evaluation of Image to Sentence Generation—0
Statistical Machine Translation with Automatic Identification of Translationese—0
USAAR-SAPE: An English--Spanish Statistical Automatic Post-Editing System—0
Reading metrics for estimating task efficiency with MT output—0
Pronoun Translation and Prediction with or without Coreference Links—0
Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation—0
Proceedings of the Second Workshop on Discourse in Machine Translation—0
Predicting Pronoun Translation Using Syntactic, Morphological and Contextual Features from Parallel Data—0
Predicting Pronouns across Languages with Continuous Word Spaces—0
Part-of-Speech Driven Cross-Lingual Pronoun Prediction with Feed-Forward Neural Networks—0
A Proposal for a Coherence Corpus in Machine Translation—0
Exploration of Inter- and Intralingual Variation of Discourse Phenomena—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