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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 4801–4850 of 10752 papers

TitleStatusHype
Improving Transformer Models by Reordering their SublayersCode0
Translationese as a Language in "Multilingual" NMT—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
A Reinforced Generation of Adversarial Examples for Neural Machine TranslationCode0
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning—0
Learning to Copy for Automatic Post-EditingCode0
Zero-Shot Paraphrase Generation with Multilingual Language Models—0
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine TranslationCode0
Pretrained Language Models for Document-Level Neural Machine Translation—0
Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates—0
Domain Robustness in Neural Machine TranslationCode0
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation—0
Can Neural Networks Learn Symbolic Rewriting?—0
Transition-Based Deep Input LinearizationCode0
Understanding Knowledge Distillation in Non-autoregressive Machine Translation—0
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding—0
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain MixingCode0
Boosting LSTM Performance Through Dynamic Precision Selection—0
The LIG system for the English-Czech Text Translation Task of IWSLT 2019—0
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation—0
Improving Grammatical Error Correction with Machine Translation PairsCode0
Probing Contextualized Sentence Representations with Visual Awareness—0
Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation—0
Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information—0
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition—0
Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views—0
Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)—0
Data Diversification: A Simple Strategy For Neural Machine TranslationCode1
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation—0
On Compositionality in Neural Machine Translation—0
Analysing Coreference in Transformer Outputs—0
Machine Translation in Pronunciation Space—0
Controlling Text Complexity in Neural Machine TranslationCode0
Machine Translation Evaluation using Bi-directional Entailment—0
CMU’s Machine Translation System for IWSLT 2019—0
Efficient Bilingual Generalization from Neural Transduction Grammar Induction—0
Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs—0
Lexical Micro-adaptation for Neural Machine Translation—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Analysis of Positional Encodings for Neural Machine Translation—0
Generic and Specialized Word Embeddings for Multi-Domain Machine Translation—0
Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training—0
On the Relation between Position Information and Sentence Length in Neural Machine Translation—0
INMT: Interactive Neural Machine Translation PredictionCode0
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
Exploiting Monolingual Data at Scale for Neural Machine Translation—0
On the Linguistic Representational Power of Neural Machine Translation Models—0
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization—0
Self-Adaptive Scaling for Learnable Residual Structure—0
Recurrent Positional Embedding for Neural Machine Translation—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