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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 6601–6650 of 10752 papers

TitleStatusHype
Can Neural Machine Translation be Improved with User Feedback?—0
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation—0
Demo of Sanskrit-Hindi SMT System—0
Pieces of Eight: 8-bit Neural Machine Translation—0
Generating Multilingual Parallel Corpus Using Subtitles—0
Cortex Neural Network: learning with Neural Network groups—0
Guiding Neural Machine Translation with Retrieved Translation Pieces—0
Domain Adaptation for Statistical Machine Translation—0
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts—0
Marian: Fast Neural Machine Translation in C++Code0
Training Tips for the Transformer ModelCode0
Fine-Grained Attention Mechanism for Neural Machine Translation—0
Identifying Semantic Divergences in Parallel Text without AnnotationsCode0
Demystifying Differentiable Programming: Shift/Reset the Penultimate BackpropagatorCode0
Low-Resource Speech-to-Text Translation—0
Quality expectations of machine translation—0
Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation—0
English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach—0
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style TransferCode0
Tensor2Tensor for Neural Machine TranslationCode0
TBD: Benchmarking and Analyzing Deep Neural Network Training—0
Achieving Human Parity on Automatic Chinese to English News TranslationCode0
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningCode0
From Nodes to Networks: Evolving Recurrent Neural Networks—0
Fast Decoding in Sequence Models using Discrete Latent Variables—0
The Importance of Being Recurrent for Modeling Hierarchical StructureCode0
Self-Attention with Relative Position RepresentationsCode0
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial ExamplesCode0
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches—0
Using Morphemes from Agglutinative Languages like Quechua and Finnish to Aid in Low-Resource Translation—0
The Impact of Advances in Neural and Statistical MT on the Translation Workforce—0
Tibetan-Chinese Neural Machine Translation based on Syllable Segmentation—0
Tutorial: MQM-DQF: A Good Marriage (Translation Quality for the 21st Century)—0
Tutorial: Corpora Quality Management for MT - Practices and Roles—0
The Sockeye Neural Machine Translation Toolkit at AMTA 2018—0
Translation Quality Metrics—0
Tutorial: De-mystifying Neural MT—0
A Smorgasbord of Features to Combine Phrase-Based and Neural Machine Translation—0
Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)—0
An Evaluation of Two Vocabulary Reduction Methods for Neural Machine Translation—0
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output—0
Joint Training for Neural Machine Translation Models with Monolingual Data—0
Simultaneous Translation using Optimized Segmentation—0
Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)—0
Keynote: Machine Translation Beyond the Sentence—0
Keynote: Setting up a Machine Translation Program for IARPA—0
Keynote: Unveiling the Linguistic Weaknesses of Neural MT—0
Keynote: Use more Machine Translation and Keep Your Customers Happy—0
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 2: User Papers)—0
Language Codes—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