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 6801–6850 of 10752 papers

TitleStatusHype
Scheduled Multi-Task Learning: From Syntax to Translation—0
Scientific registers and disciplinary diversification: a comparable corpus approach—0
SciPar: A Collection of Parallel Corpora from Scientific Abstracts—0
Screening Gender Transfer in Neural Machine Translation—0
SCTB: A Chinese Treebank in Scientific Domain—0
Search-Aware Tuning for Hierarchical Phrase-based Decoding—0
Search-Aware Tuning for Machine Translation—0
Search Engine Guided Non-Parametric Neural Machine Translation—0
Searching for COMETINHO: The Little Metric That Could—0
Searching for Context: a Study on Document-Level Labels for Translation Quality Estimation—0
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability—0
Secoco: Self-Correcting Encoding for Neural Machine Translation—0
Segmentation for Domain Adaptation in Arabic—0
Segmentation-Free Word Embedding for Unsegmented Languages—0
Segmentation Strategies for Streaming Speech Translation—0
Segment-Based Interactive Machine Translation for Pre-trained Models—0
Segmenting Subtitles for Correcting ASR Segmentation Errors—0
Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia—0
SEJFEK - a Lexicon and a Shallow Grammar of Polish Economic Multi-Word Units—0
Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation—0
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation—0
Selecting Data for English-to-Czech Machine Translation—0
Selecting Feature Sets for Comparative and Time-Oriented Quality Estimation of Machine Translation Output—0
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System—0
Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking—0
Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation—0
Selective Combination of Pivot and Direct Statistical Machine Translation Models—0
Selective Data Augmentation for Robust Speech Translation—0
Selective Decoding for Cross-lingual Open Information Extraction—0
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation—0
Self-Adaptive Scaling for Learnable Residual Structure—0
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems—0
Self-Attention Networks for Intent Detection—0
Self-Attention with Cross-Lingual Position Representation—0
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation—0
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages—0
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation—0
Self-Evolution Knowledge Distillation for LLM-based Machine Translation—0
Self-Guided Curriculum Learning for Neural Machine Translation—0
Self-Induced Curriculum Learning in Neural Machine Translation—0
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation—0
Self-Knowledge Distillation in Natural Language Processing—0
Self-Learning for Zero Shot Neural Machine Translation—0
Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations—0
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation—0
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation—0
Self-Supervised Learning for Pairwise Data Refinement—0
Self-Supervised Quality Estimation for Machine Translation—0
Self Syntactico-Semantic Enrichment of LMF Normalized Dictionaries—0
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios—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