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 7051–7100 of 10752 papers

TitleStatusHype
Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise—0
Improving MT System Using Extracted Parallel Fragments of Text from Comparable Corpora—0
Improving Multi-Head Attention with Capsule Networks—0
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French,English - Vietnamese—0
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese—0
Improving Multilingual Neural Machine Translation System for Indic Languages—0
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages—0
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling—0
Improving Neural Abstractive Document Summarization with Structural Regularization—0
Improving Neural Language Generation with Spectrum Control—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation—0
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features—0
Improving Neural Machine Translation by Bidirectional Training—0
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning—0
Improving Neural Machine Translation by Denoising Training—0
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting—0
Improving Neural Machine Translation for Sanskrit-English—0
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Improving Neural Machine Translation on resource-limited pairs using auxiliary data of a third language—0
Improving Neural Machine Translation through Phrase-based Forced Decoding—0
Improving Neural Machine Translation Using Noisy Parallel Data through Distillation—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Improving Neural Machine Translation with Neural Syntactic Distance—0
Improving Neural Machine Translation with Soft Template Prediction—0
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels—0
Improving Neural Text Simplification Model with Simplified Corpora—0
Improving Neural Translation Models with Linguistic Factors—0
Improving NMT Quality Using Terminology Injection—0
Improving NMT via Filtered Back Translation—0
Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization—0
Improving Non-autoregressive Neural Machine Translation with Monolingual Data—0
Improving Non-Autoregressive Neural Machine Translation via Modeling Localness—0
Improving Non-Autoregressive Translation Models Without Distillation—0
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing—0
Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models—0
Improving Paraphrase Generation models with machine translation generated pre-training—0
Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese--Japanese—0
Improving Phrase-Based SMT Using Cross-Granularity Embedding Similarity—0
Improving Pivot-Based Statistical Machine Translation Using Random Walk—0
Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs—0
Improving Pivot Translation by Remembering the Pivot—0
Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similarity—0
Improving Precision of Grammatical Error Correction with a Cheat Sheet—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
Improving Pronoun Translation by Modeling Coreference Uncertainty—0
Improving Pronoun Translation for Statistical Machine Translation—0
Improving Relative-Entropy Pruning using Statistical Significance—0
Improving reordering performance using higher order and structural features—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