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 9501–9550 of 10752 papers

TitleStatusHype
CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval—0
CUNI Submission to MT4All Shared Task—0
CUNI Submission to the BUCC 2022 Shared Task on Bilingual Term Alignment—0
CUNI System for the WMT17 Multimodal Translation Task—0
CUNI System for the WMT18 Multimodal Translation Task—0
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks—0
CUNI System for WMT17 Automatic Post-Editing Task—0
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20—0
CUNI Systems for the Unsupervised News Translation Task in WMT 2019—0
CUNI Systems for the Unsupervised News Translation Task in WMT 2019—0
CUNI Systems for the WMT22 Czech-Ukrainian Translation Task—0
CUNI Transformer Neural MT System for WMT18—0
Curb Your Carbon Emissions: Benchmarking Carbon Emissions in Machine Translation—0
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale—0
Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English—0
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review—0
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation—0
Curriculum Learning for Domain Adaptation in Neural Machine Translation—0
Curvature: A signature for Action Recognition in Video Sequences—0
Customizable SCF Acquisition in Italian—0
Customization of the Europarl Corpus for Translation Studies—0
Customized Neural Machine Translation Systems for the Swiss Legal Domain—0
Customizing Neural Machine Translation for Subtitling—0
Concealed Data Poisoning Attacks on NLP Models—0
Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation—0
Cutting the Long Tail: Hybrid Language Models for Translation Style Adaptation—0
CVIT-MT Systems for WAT-2018—0
CVIT's submissions to WAT-2019—0
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices—0
Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study—0
Cyclegan Network for Sheet Metal Welding Drawing Translation—0
DAG-Structured Long Short-Term Memory for Semantic Compositionality—0
DAHRS: Divergence-Aware Hallucination-Remediated SRL Projection—0
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation—0
Dangerous Relations in Dependency Treebanks—0
DanProof: Pedagogical Spell and Grammar Checking for Danish—0
DAQE: Exploring the Direct Assessment on Word-Level Quality Estimation in Machine Translation—0
Dashboard: A Tool for Integration, Validation, and Visualization of Distributed NLP Systems on Heterogeneous Platforms—0
Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican—0
Data-adaptive Transfer Learning for Low-resource Translation: A Case Study in Haitian—0
Data and Parameter Scaling Laws for Neural Machine Translation—0
Data Augmentation and Terminology Integration for Domain-Specific Sinhala-English-Tamil Statistical Machine Translation—0
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Data Augmentation for Neural Machine Translation using Generative Language Model—0
Data Augmentation for Sign Language Gloss Translation—0
Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study—0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Data augmentation using back-translation for context-aware 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
5AdminBLEU score43.8—Unverified
6Transformer (ADMIN init)BLEU 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