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
Learning Source Phrase Representations for Neural Machine Translation—0
Learning Structured Predictors from Bandit Feedback for Interactive NLP—0
Learning synchronous context-free grammars with multiple specialised non-terminals for hierarchical phrase-based translation—0
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network—0
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network—0
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
Learning the Taxonomy of Function Words for Parsing—0
Learning to Actively Learn Neural Machine Translation—0
Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis—0
Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study—0
Learning to Combine Instructions in LLVM Compiler—0
Learning to Control the Specificity in Neural Response Generation—0
Learning to Decode for Future Success—0
Learning to Detect Unacceptable Machine Translations for Downstream Tasks—0
Learning to Differentiate Better from Worse Translations—0
Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation—0
Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task—0
Learning to Find Translations and Transliterations on the Web—0
Learning to Find Translations and Transliterations on the Web based on Conditional Random Fields—0
Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation—0
Learning to Interpret and Describe Abstract Scenes—0
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism—0
Learning to Make Inferences in a Semantic Parsing Task—0
Learning to Model Multilingual Unrestricted Coreference in OntoNotes—0
Learning to Multi-Task Learn for Better Neural Machine Translation—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
Learning Topic Representation for SMT with Neural Networks—0
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing—0
Learning to Refine Source Representations for Neural Machine Translation—0
Learning to Represent Words in Context with Multilingual Supervision—0
Learning to Reuse Translations: Guiding Neural Machine Translation with Examples—0
Learning to Scale Multilingual Representations for Vision-Language Tasks—0
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks—0
Learning to Segment Inputs for NMT Favors Character-Level Processing—0
Learning to Start for Sequence to Sequence Architecture—0
Learning to Stop in Structured Prediction for Neural Machine Translation—0
Learning to Reweight with Deep Interactions—0
Learning to Teach with Dynamic Loss Functions—0
Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval—0
Learning to Translate with Multiple Objectives—0
Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
Learning Translation Consensus with Structured Label Propagation—0
Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models—0
Learning Translations for Tagged Words: Extending the Translation Lexicon of an ITG for Low Resource Languages—0
Learning Translations via Images with a Massively Multilingual Image Dataset—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Learning Using 1-Local Membership Queries—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