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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 5301–5350 of 10752 papers

TitleStatusHype
EED: Extended Edit Distance Measure for Machine Translation—0
SAO WMT19 Test Suite: Machine Translation of Audit ReportsCode0
Customizing Neural Machine Translation for Subtitling—0
Neural machine translation system for the Kazakh language—0
Filling Gender \& Number Gaps in Neural Machine Translation with Black-box Context Injection—0
Machine Translation for Crimean Tatar to Turkish—0
NICT's Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task—0
Integration of Dubbing Constraints into Machine Translation—0
Machine Translation with parfda, Moses, kenlm, nplm, and PRO—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task—0
A step towards Torwali machine translation: an analysis of morphosyntactic challenges in a low-resource language—0
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task—0
Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning—0
NICT's Machine Translation Systems for the WMT19 Similar Language Translation Task—0
Kyoto University Participation to the WMT 2019 News Shared Task—0
RTM Stacking Results for Machine Translation Performance Prediction—0
Decision-making, Risk, and Gist Machine Translation in the Work of Patent Professionals—0
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation—0
Assessing the Ability of Neural Machine Translation Models to Perform Syntactic Rewriting—0
Assessing Back-Translation as a Corpus Generation Strategy for non-English Tasks: A Study in Reading Comprehension and Word Sense Disambiguation—0
Quality and Coverage: The AFRL Submission to the WMT19 Parallel Corpus Filtering for Low-Resource Conditions Task—0
Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings—0
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
Leveraging SNOMED CT terms and relations for machine translation of clinical texts from Basque to Spanish—0
Pivot Machine Translation in INTERACT Project—0
Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models—0
JUMT at WMT2019 News Translation Task: A Hybrid approach to Machine Translation for Lithuanian to English—0
Meteor++ 2.0: Adopt Syntactic Level Paraphrase Knowledge into Machine Translation Evaluation—0
JUCBNMT at WMT2018 News Translation Task: Character Based Neural Machine Translation of Finnish to English—0
Baidu Neural Machine Translation Systems for WMT19—0
Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation—0
An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation—0
Hungarian translators' perceptions of Neural Machine Translation in the European Commission—0
JU-Saarland Submission to the WMT2019 English--Gujarati Translation Shared Task—0
Improving Precision of Grammatical Error Correction with a Cheat Sheet—0
Improving Neural Machine Translation Using Noisy Parallel Data through Distillation—0
Morphology-aware Word-Segmentation in Dialectal Arabic Adaptation of Neural Machine Translation—0
The AFRL WMT19 Systems: Old Favorites and New Tricks—0
An Exploration of Placeholding in Neural Machine Translation—0
Controlling the Reading Level of Machine Translation Output—0
The Challenges of Using Neural Machine Translation for Literature—0
Incorporating Source Syntax into Transformer-Based Neural Machine Translation—0
Machine Translation in the Financial Services Industry: A Case Study—0
Sentence-Level Adaptation for Low-Resource Neural Machine Translation—0
System Description: The Submission of FOKUS to the WMT 19 Robustness Task—0
Improving Robustness of Neural Machine Translation with Multi-task LearningCode0
GTCOM Neural Machine Translation Systems for WMT19—0
Competitiveness Analysis of the European Machine Translation Market—0
Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and 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