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 2751–2800 of 10752 papers

TitleStatusHype
A Preliminary Study on Mandarin-Hakka neural machine translation using small-sized data—0
A joint inference of deep case analysis and zero subject generation for Japanese-to-English statistical machine translation—0
Capsule-Transformer for Neural Machine Translation—0
A Preliminary Study of Croatian Lexical Substitution—0
Can You Traducir This? Machine Translation for Code-Switched Input—0
A Preliminary Study for Building an Arabic Corpus of Pair Questions-Texts from the Web: AQA-Webcorp—0
A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation—0
Adaptation of Reordering Models for Statistical Machine Translation—0
Accurate Evaluation of Segment-level Machine Translation Metrics—0
Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation—0
A Practical Chinese-English ON Translation Method Based on ON`s Distribution Characteristics on the Web—0
Can Word Segmentation be Considered Harmful for Statistical Machine Translation Tasks between Japanese and Chinese?—0
Approximate Sentence Retrieval for Scalable and Efficient Example-Based Machine Translation—0
A Joint Chinese Named Entity Recognition and Disambiguation System—0
Approximate Distribution Matching for Sequence-to-Sequence Learning—0
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN—0
Approches quantitatives de l'analyse des prédictions en traduction automatique neuronale (TAN)—0
A Joint Approach to Compound Splitting and Idiomatic Compound Detection—0
Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique—0
Can the Variation of Model Weights be used as a Criterion for Self-Paced Multilingual NMT?—0
Can the Language of the Collation be Translated into the Language of the Stemma? Using Machine Translation for Witness Localization—0
Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task—0
A Japanese Word Dependency Corpus—0
Can Text Simplification Help Machine Translation?—0
Can Statistical Post-Editing with a Small Parallel Corpus Save a Weak MT Engine?—0
Can SMT and RBMT Improve each other's Performance?- An Experiment with English-Hindi Translation—0
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation—0
Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task—0
Airport Announcement System for Deaf—0
Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods—0
Accurate Cross-lingual Projection between Count-based Word Vectors by Exploiting Translatable Context Pairs—0
A Bayesian approach to translators' reliability assessment—0
Findings of the Third Workshop on Neural Generation and Translation—0
Development of Deep Learning Based Natural Language Processing Model for Turkish—0
Can neural networks predict dynamics they have never seen?—0
Can Neural Networks Learn Symbolic Rewriting?—0
Approaching English-Polish Machine Translation Quality Assessment with Neural-based Methods—0
Can neural machine translation do simultaneous translation?—0
Can Neural Machine Translation be Improved with User Feedback?—0
Appraise Evaluation Framework for Machine Translation—0
AI in Support of Diversity and Inclusion—0
Can Multilinguality Benefit Non-autoregressive Machine Translation?—0
Can Multilinguality benefit Non-autoregressive Machine Translation?—0
Applying the Cognitive Machine Translation Evaluation Approach to Arabic—0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
Can Machine Learning Algorithms Improve Phrase Selection in Hybrid Machine Translation?—0
Applying Statistical Post-Editing to English-to-Korean Rule-based Machine Translation System—0
Adaptation of a Rule-Based Translator to R\' de la Plata Spanish—0
Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?—0
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing 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