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 7701–7750 of 10752 papers

TitleStatusHype
Iterative Refinement for Machine Translation—0
Neural Machine Translation with Characters and Hierarchical EncodingCode0
Addressing Community Question Answering in English and Arabic—0
Personalized Machine Translation: Preserving Original Author Traits—0
SYSTRAN's Pure Neural Machine Translation Systems—0
Interactive Attention for Neural Machine Translation—0
Pre-Translation for Neural Machine Translation—0
Neural Machine Translation Advised by Statistical Machine Translation—0
Translation Quality Estimation using Recurrent Neural Network—0
Fast, Scalable Phrase-Based SMT Decoding—0
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation—0
Fully Character-Level Neural Machine Translation without Explicit SegmentationCode0
Enabling Medical Translation for Low-Resource Languages—0
Morphology Generation for Statistical Machine Translation using Deep Learning Techniques—0
Challenges of Computational Processing of Code-Switching—0
Scalable Machine Translation in Memory Constrained EnvironmentsCode0
Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?Code0
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation DirectionsCode0
Learning to Translate in Real-time with Neural Machine TranslationCode0
Proceedings of the 2nd Deep Machine Translation Workshop—0
Building a Translation Memory to Improve Machine Translation Coverage and Quality—0
Computing Sentiment Scores of Verb Phrases for Vietnamese—0
Improving Neural Machine Translation on resource-limited pairs using auxiliary data of a third language—0
Retrieval Term Prediction Using Deep Learning Methods—0
Neural Interactive Translation Prediction—0
Recurrent Neural Network Based Loanwords Identification in Uyghur—0
A Generalized Framework for Hierarchical Word Sequence Language Model—0
Incorporation of a valency lexicon into a TectoMT pipeline—0
Bilingual Methods for Adaptive Training Data Selection for Machine Translation—0
Adding syntactic structure to bilingual terminology for improved domain adaptation—0
Automatic Construction of Morphologically Motivated Translation Models for Highly Inflected, Low-Resource LanguagesCode0
Factoring Adjunction in Hierarchical Phrase-Based SMT—0
SMTPOST Using Statistical Machine Translation Approach in Filipino Part-of-Speech Tagging—0
A Hybrid Approach for Deep Machine Translation—0
Instance Selection for Online Automatic Post-Editing in a multi-domain scenario—0
Korean Language Resources for Everyone—0
Multilingual Search with Machine Translation in the Intel Communities—0
Dealing with Out-Of-Vocabulary Problem in Sentence Alignment Using Word SimilarityCode0
Investigating the Impact of Various Partial Diacritization Schemes on Arabic-English Statistical Machine Translation—0
Multi-domain Adaptation for Statistical Machine Translation Based on Feature Augmentation—0
Moses \& Treex Hybrid MT Systems Bestiary—0
Deeper Machine Translation and Evaluation for German—0
HSSA tree structures for BTG-based preordering in machine translation—0
Machine Translation Acceptance Among Professional Linguists: Are We Nearing the Tipping Point?—0
Fuzzy-match repair using black-box machine translation systems: what can be expected?—0
Machine Translation Quality and Post-Editor Productivity—0
Planting Trees in the Desert: Delexicalized Tagging and Parsing Combined—0
Ranking suggestions for black-box interactive translation prediction systems with multilayer perceptrons—0
Yet Another Symmetrical and Real-time Word Alignment Method: Hierarchical Sub-sentential Alignment using F-measure—0
Vocabulary Selection Strategies for Neural 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