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 10451–10500 of 10752 papers

TitleStatusHype
UPM system for WMT 2012—0
The CMU-Avenue French-English Translation System—0
Using Categorial Grammar to Label Translation Rules—0
Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing—0
SPEDE: Probabilistic Edit Distance Metrics for MT Evaluation—0
Comparing human perceptions of post-editing effort with post-editing operations—0
DFKI's SMT System for WMT 2012—0
LORIA System for the WMT12 Quality Estimation Shared Task—0
Proceedings of the Seventh Workshop on Statistical Machine Translation—0
PRHLT Submission to the WMT12 Quality Estimation Task—0
Match without a Referee: Evaluating MT Adequacy without Reference Translations—0
DCU-Symantec Submission for the WMT 2012 Quality Estimation Task—0
QCRI at WMT12: Experiments in Spanish-English and German-English Machine Translation of News Text—0
Selecting Data for English-to-Czech Machine Translation—0
Probes in a Taxonomy of Factored Phrase-Based Models—0
Evaluating the Learning Curve of Domain Adaptive Statistical Machine Translation Systems—0
Leave-One-Out Phrase Model Training for Large-Scale Deployment—0
CCG Syntactic Reordering Models for Phrase-based Machine Translation—0
On Hierarchical Re-ordering and Permutation Parsing for Phrase-based Decoding—0
Data Issues of the Multilingual Translation Matrix—0
Formemes in English-Czech Deep Syntactic MT—0
Kriya - The SFU System for Translation Task at WMT-12—0
Findings of the 2012 Workshop on Statistical Machine Translation—0
Linguistic Features for Quality Estimation—0
LIMSI @ WMT12—0
Quality estimation for Machine Translation output using linguistic analysis and decoding features—0
PROMT DeepHybrid system for WMT12 shared translation task—0
Semantic Textual Similarity for MT evaluation—0
Class error rates for evaluation of machine translation output—0
Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives—0
Improving AMBER, an MT Evaluation Metric—0
GHKM Rule Extraction and Scope-3 Parsing in Moses—0
Combining Quality Prediction and System Selection for Improved Automatic Translation Output—0
Joshua 4.0: Packing, PRO, and Paraphrases—0
Analysing the Effect of Out-of-Domain Data on SMT Systems—0
Joint WMT 2012 Submission of the QUAERO Project—0
Fully Automatic Semantic MT Evaluation—0
Morpheme- and POS-based IBM1 and language model scores for translation quality estimation—0
LIUM's SMT Machine Translation Systems for WMT 2012—0
Black Box Features for the WMT 2012 Quality Estimation Shared Task—0
Quality Estimation: an experimental study using unsupervised similarity measures—0
Machine Learning for Hybrid Machine Translation—0
Direct Error Rate Minimization for Statistical Machine Translation—0
Review of Hypothesis Alignment Algorithms for MT System Combination via Confusion Network Decoding—0
Non-Linear Models for Confidence Estimation—0
Regression with Phrase Indicators for Estimating MT Quality—0
DEPFIX: A System for Automatic Correction of Czech MT Outputs—0
Putting Human Assessments of Machine Translation Systems in Order—0
Optimization Strategies for Online Large-Margin Learning in Machine Translation—0
PREFER: Using a Graph-Based Approach to Generate Paraphrases for Language Learning—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