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

Showing 55515600 of 10752 papers

TitleStatusHype
Improving Domain Adaptation Translation with Domain Invariant and Specific Information0
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language ResearchCode1
Parallelizable Stack Long Short-Term MemoryCode0
Modeling Recurrence for Transformer0
Information Aggregation for Multi-Head Attention with Routing-by-Agreement0
Convolutional Self-Attention Networks0
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation0
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems0
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation0
End-to-End Video Captioning0
Consistency by Agreement in Zero-shot Neural Machine TranslationCode0
A Large-Scale Comparison of Historical Text Normalization SystemsCode0
Multimodal Machine Translation with Embedding PredictionCode0
Learning to Stop in Structured Prediction for Neural Machine Translation0
Machine translation considering context information using Encoder-Decoder model0
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition0
Using Monolingual Data in Neural Machine Translation: a Systematic StudyCode0
ner and pos when nothing is capitalized0
Interoperability and machine-to-machine translation model with mappings to machine learning tasks0
Neural Grammatical Error Correction with Finite State Transducers0
Neural Program Planner for Structured Predictions0
Competence-based Curriculum Learning for Neural Machine TranslationCode0
Pre-trained Language Model Representations for Language Generation0
Selective Attention for Context-aware Neural Machine TranslationCode0
Probing the Need for Visual Context in Multimodal Machine Translation0
CVIT-MT Systems for WAT-20180
compare-mt: A Tool for Holistic Comparison of Language Generation SystemsCode0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
The Missing Ingredient in Zero-Shot Neural Machine Translation0
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence ModelsCode0
Maybe Deep Neural Networks are the Best Choice for Modeling Source CodeCode0
Bootstrapping Method for Developing Part-of-Speech Tagged Corpus in Low Resource Languages Tagset - A Focus on an African Igbo0
Context-Aware Learning for Neural Machine Translation0
Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection0
Integrating Artificial and Human Intelligence for Efficient Translation0
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNNCode0
Polylingual Wordnet0
Calibration of Encoder Decoder Models for Neural Machine Translation0
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus0
Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information0
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled DataCode0
Non-Parametric Adaptation for Neural Machine Translation0
Evaluating Rewards for Question Generation ModelsCode0
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation0
Massively Multilingual Neural Machine Translation0
Multilingual Neural Machine Translation with Knowledge DistillationCode0
EAT: a simple and versatile semantic representation format for multi-purpose NLP0
Using Deep Object Features for Image Descriptions0
Lost in Machine Translation: A Method to Reduce Meaning LossCode0
Improving Robustness of Machine Translation with Synthetic NoiseCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer Cycle (Rev)BLEU score35.14Unverified
2Noisy back-translationBLEU score35Unverified
3Transformer+Rep(Uni)BLEU score33.89Unverified
4T5-11BBLEU score32.1Unverified
5BiBERTBLEU score31.26Unverified
6Transformer + R-DropBLEU score30.91Unverified
7Bi-SimCutBLEU score30.78Unverified
8BERT-fused NMTBLEU score30.75Unverified
9Data Diversification - TransformerBLEU score30.7Unverified
10SimCutBLEU score30.56Unverified
#ModelMetricClaimedVerifiedStatus
1Transformer+BT (ADMIN init)BLEU score46.4Unverified
2Noisy back-translationBLEU score45.6Unverified
3mRASP+Fine-TuneBLEU score44.3Unverified
4Transformer + R-DropBLEU score43.95Unverified
5AdminBLEU score43.8Unverified
6Transformer (ADMIN init)BLEU score43.8Unverified
7BERT-fused NMTBLEU score43.78Unverified
8MUSE(Paralllel Multi-scale Attention)BLEU score43.5Unverified
9T5BLEU score43.4Unverified
10Local Joint Self-attentionBLEU score43.3Unverified
#ModelMetricClaimedVerifiedStatus
1PiNMTBLEU score40.43Unverified
2BiBERTBLEU score38.61Unverified
3Bi-SimCutBLEU score38.37Unverified
4Cutoff + Relaxed Attention + LMBLEU score37.96Unverified
5DRDABLEU score37.95Unverified
6Transformer + R-Drop + CutoffBLEU score37.9Unverified
7SimCutBLEU score37.81Unverified
8Cutoff+KneeBLEU score37.78Unverified
9CutoffBLEU score37.6Unverified
10CipherDAugBLEU score37.53Unverified
#ModelMetricClaimedVerifiedStatus
1HWTSC-Teacher-SimScore19.97Unverified
2MS-COMET-22Score19.89Unverified
3MS-COMET-QE-22Score19.76Unverified
4KG-BERTScoreScore17.28Unverified
5metricx_xl_DA_2019Score17.17Unverified
6COMET-QEScore16.8Unverified
7COMET-22Score16.31Unverified
8UniTE-srcScore15.68Unverified
9UniTE-refScore15.38Unverified
10metricx_xxl_DA_2019Score15.24Unverified