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 7001–7050 of 10752 papers

TitleStatusHype
Improving both domain robustness and domain adaptability in machine translation—0
Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation—0
Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation—0
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation—0
Improving Character-level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature—0
Improving Chinese-English PropBank Alignment—0
Improving Chinese Grammatical Error Correction with Corpus Augmentation and Hierarchical Phrase-based Statistical Machine Translation—0
Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames—0
基於單語言機器翻譯技術改進中文文字蘊涵 (Improving Chinese Textural Entailment by Monolingual Machine Translation Technology) [In Chinese]—0
Improving Chinese-to-Japanese Patent Translation Using English as Pivot Language—0
Improving Context-aware Neural Machine Translation with Target-side Context—0
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach—0
Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention—0
Improving Document-Level Neural Machine Translation with Domain Adaptation—0
Improving Domain Adaptation for Machine Translation withTranslation Pieces—0
Improving Domain Adaptation Translation with Domain Invariant and Specific Information—0
Improving domain-specific SMT for low-resourced languages using data from different domains—0
Improving English-Russian sentence alignment through POS tagging and Damerau-Levenshtein distance—0
Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag—0
Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets—0
Improving evaluation and optimization of MT systems against MEANT—0
Improving Evaluation of Document-level Machine Translation Quality Estimation—0
Improving Evaluation of English-Czech MT through Paraphrasing—0
Improving Evaluation of Machine Translation Quality Estimation—0
Improving fast\_align by Reordering—0
Improving Fluency of Non-Autoregressive Machine Translation—0
Improving Gender Translation Accuracy with Filtered Self-Training—0
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings—0
Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters—0
Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language—0
Improving Japanese-to-English Neural Machine Translation by Voice Prediction—0
Improving Jejueo-Korean Translation With Cross-Lingual Pretraining Using Japanese and Korean—0
Improving Language Model Adaptation using Automatic Data Selection and Neural Network—0
Improving Language Model Integration for Neural Machine Translation—0
Improving Language Modelling with Noise-contrastive estimation—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
Improving Long Context Document-Level Machine Translation—0
Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)—0
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation—0
Improving Machine Translation by Searching Skip Connections Efficiently—0
Improving machine translation by training against an automatic semantic frame based evaluation metric—0
Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies—0
Improving Machine Translation of Educational Content via Crowdsourcing—0
Improving Machine Translation of English Relative Clauses with Automatic Text Simplification—0
Improving machine translation of null subjects in Italian and Spanish—0
Improving Machine Translation of Rare and Unseen Word Senses—0
Improving Machine Translation Quality Estimation with Neural Network Features—0
Improving Machine Translation with Phrase Pair Injection and Corpus Filtering—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