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 6751–6800 of 10752 papers

TitleStatusHype
KIT’s Multilingual Neural Machine Translation systems for IWSLT 2017—0
Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems—0
Overview of the IWSLT 2017 Evaluation Campaign—0
The RWTH Aachen Machine Translation Systems for IWSLT 2017—0
Three-phase training to address data sparsity in Neural Machine Translation—0
Towards better translation performance on spoken language—0
Toward Robust Neural Machine Translation for Noisy Input Sequences—0
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches—0
Parameters Optimization of Deep Learning Models using Particle Swarm Optimization—0
Modeling Past and Future for Neural Machine TranslationCode0
Neural Text Generation: A Practical Guide—0
Machine Translation using Semantic Web Technologies: A Survey—0
Learning to Remember Translation History with a Continuous CacheCode0
Counterfactual Learning for Machine Translation: Degeneracies and Solutions—0
Effective Strategies in Zero-Shot Neural Machine TranslationCode0
Evaluating Machine Translation Performance on Chinese Idioms with a Blacklist Method—0
Using stochastic computation graphs formalism for optimization of sequence-to-sequence modelCode0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks—0
Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model—0
An Encoder-Decoder Framework Translating Natural Language to Database Queries—0
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations—0
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings—0
Classical Structured Prediction Losses for Sequence to Sequence Learning—0
Syntax-Directed Attention for Neural Machine Translation—0
Document Context Neural Machine Translation with Memory Networks—0
Block-Sparse Recurrent Neural Networks—0
Non-Autoregressive Neural Machine TranslationCode0
Synthetic and Natural Noise Both Break Neural Machine TranslationCode0
A^4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation—0
Weighted Transformer Network for Machine TranslationCode0
Towards Neural Machine Translation with Partially Aligned Corpora—0
Compressing Word Embeddings via Deep Compositional Code LearningCode0
Extracting an English-Persian Parallel Corpus from Comparable Corpora—0
Neural Lattice Search for Domain Adaptation in Machine Translation—0
Supervised Attention for Sequence-to-Sequence Constituency Parsing—0
Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation—0
Evaluating Discourse Phenomena in Neural Machine Translation—0
A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task—0
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information—0
Arabic-English Text Translation Leveraging Hybrid NER—0
NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project—0
Lexical Simplification with the Deep Structured Similarity Model—0
CUNI NMT System for WAT 2017 Translation Tasks—0
Geographical Evaluation of Word Embeddings—0
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017—0
Substring Frequency Features for Segmentation of Japanese Katakana Words with Unlabeled Corpora—0
NTT Neural Machine Translation Systems at WAT 2017—0
A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch SizeCode0
Kyoto University Participation to WAT 2017Code0
BTG-based Machine Translation with Simple Reordering Model using Structured Perceptron—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