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 7101–7150 of 10752 papers

TitleStatusHype
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions—0
Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data—0
Improving Robustness in Multilingual Machine Translation via Data Augmentation—0
Improving Robustness in Real-World Neural Machine Translation Engines—0
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions—0
Improving Semantic Parsing with Enriched Synchronous Context-Free Grammar—0
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning—0
Improving Sentiment Analysis in Twitter Using Multilingual Machine Translated Data—0
Improving Sequence to Sequence Learning for Morphological Inflection Generation: The BIU-MIT Systems for the SIGMORPHON 2016 Shared Task for Morphological Reinflection—0
Improving Sequence-to-Sequence Learning via Optimal Transport—0
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information—0
Improving Similar Language Translation With Transfer Learning—0
Improving Speech-to-Speech Translation Through Unlabeled Text—0
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
Improving speech translation by fusing speech and text—0
Improving Statistical Machine Translation with a Multilingual Paraphrase Database—0
Improving Statistical Machine Translation with Word Class Models—0
Improving Statistical Machine Translation Accuracy Using Bilingual Lexicon Extractionwith Paraphrases—0
Improving Statistical Machine Translation with Processing Shallow Parsing—0
Improving Statistical Machine Translation Performance by Oracle-BLEU Model Re-estimation—0
Improving Statistical Machine Translation through co-joining parts of verbal constructs in English-Hindi translation—0
Improving Statistical Machine Translation with Selectional Preferences—0
Improving Statistical Machine Translation by Adapting Translation Models to Translationese—0
Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages—0
Improving Syntax-Augmented Machine Translation by Coarsening the Label Set—0
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation—0
Improving Text Generation with Student-Forcing Optimal Transport—0
Improving Text Normalization using Character-Blocks Based Models and System Combination—0
Improving Text Normalization via Unsupervised Model and Discriminative Reranking—0
Improving Text Simplification Language Modeling Using Unsimplified Text Data—0
Improving the IBM Alignment Models Using Variational Bayes—0
Improving the Performance of an Example-Based Machine Translation System Using a Domain-specific Bilingual Lexicon—0
Improving the Performance of English-Tamil Statistical Machine Translation System using Source-Side Pre-Processing—0
Improving the Performance of Neural Machine Translation Involving Morphologically Rich Languages—0
Improving the precision of automatically constructed human-oriented translation dictionaries—0
Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration—0
Improving the Quality of MT Output using Novel Name Entity Translation Scheme—0
Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entities—0
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation—0
Improving the Robustness of Speech Translation—0
Improving the Translation of Discourse Markers for Chinese into English—0
Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation—0
Improving Translation Selection with Supersenses—0
Improving Translation to Morphologically Rich Languages (Am\'eliorer la traduction des langages morphologiquement riches) [in French]—0
MuMUR : Multilingual Multimodal Universal Retrieval—0
Improving Vietnamese-English Medical Machine Translation—0
Improving word alignment for low resource languages using English monolingual SRL—0
Improving Word Alignment of Rare Words with Word Embeddings—0
Improving Word Alignment Using Linguistic Code Switching Data—0
Improving Word Alignment using Word Similarity—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