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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 8151–8200 of 10752 papers

TitleStatusHype
Measuring the behavioral impact of machine translation quality improvements with A/B testing—0
Measuring the Divergence of Dependency Structures Cross-Linguistically to Improve Syntactic Projection Algorithms—0
Measuring the Effect of Conversational Aspects on Machine Translation Quality—0
Measuring the Effects of Human and Machine Translation on Website Engagement—0
Measuring the Impact of Spelling Errors on the Quality of Machine Translation—0
Measuring the Influence of Long Range Dependencies with Neural Network Language Models—0
Measuring the Limit of Semantic Divergence for English Tweets.—0
Measuring Translationese across Levels of Expertise: Are Professionals more Surprising than Students?—0
Measuring Uncertainty in Translation Quality Evaluation (TQE)—0
MEDLINE as a Parallel Corpus: a Survey to Gain Insight on French-, Spanish- and Portuguese-speaking Authors' Abstract Writing Practice—0
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection—0
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation—0
MeetDot: Videoconferencing with Live Translation Captions—0
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation—0
Memorization or Reasoning? Exploring the Idiom Understanding of LLMs—0
Memory-augmented Chinese-Uyghur Neural Machine Translation—0
Memory-augmented Neural Machine Translation—0
Memory Efficient Adaptive Optimization—0
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model—0
Memory-enhanced Decoder for Neural Machine Translation—0
Memory Representation in Transformer—0
Mention Attention for Pronoun Translation—0
Merged bilingual trees based on Universal Dependencies in Machine Translation—0
Mergen: The First Manchu-Korean Machine Translation Model Trained on Augmented Data—0
Merging External Bilingual Pairs into Neural Machine Translation—0
Merging Verb Senses of Hindi WordNet using Word Embeddings—0
Merging Word Senses—0
Meta Back-translation—0
Meta-Embeddings Based On Self-Attention—0
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020—0
Metagross: Meta Gated Recursive Controller Units for Sequence Modeling—0
Metaheuristic Approaches to Lexical Substitution and Simplification—0
Meta Learning and Its Applications to Natural Language Processing—0
Meta-Learning for Few-Shot NMT Adaptation—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning—0
Meta-level Statistical Machine Translation—0
MetaMind Neural Machine Translation System for WMT 2016—0
MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
Metaphor Identification as Interpretation—0
Meteor++ 2.0: Adopt Syntactic Level Paraphrase Knowledge into Machine Translation Evaluation—0
Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation—0
Meteor Universal: Language Specific Translation Evaluation for Any Target Language—0
METEOR-WSD: Improved Sense Matching in MT Evaluation—0
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations—0
Metrics for Evaluation of Word-level Machine Translation Quality Estimation—0
Microblogs as Parallel Corpora—0
Microsoft Speech Language Translation (MSLT) Corpus: The IWSLT 2016 release for English, French and German—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