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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 2651–2700 of 10752 papers

TitleStatusHype
On “Human Parity” and “Super Human Performance” in Machine Translation Evaluation—0
The Nós Project: Opening routes for the Galician language in the field of language technologies—0
Flux d’informations dans les systèmes encodeur-décodeur. Application à l’explication des biais de genre dans les systèmes de traduction automatique. (Information flow in encoder-decoder systems applied to the explanation of gender bias in machine translation systems)—0
Europeana Translate: Providing multilingual access to digital cultural heritage—0
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text—0
Mean Machine Translations: On Gender Bias in Icelandic Machine Translations—0
Writing in a second Language with Machine translation (WiLMa)—0
Sign Language Translation: Ongoing Development, Challenges and Innovations in the SignON Project—0
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot—0
GRhOOT: Ontology of Rhetorical Figures in GermanCode0
Système de traduction automatique neuronale français-mongol (Historique, mise en place et évaluations) (French-Mongolian Neural Machine Translation System (History, Implementation, and evaluations) Machine Translation (hereafter abbreviated MT) is currently undergoing rapid development, during which less-resourced languages nevertheless seem to be less developed)—0
CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT—0
MTrill: Machine Translation Impact on Language Learning—0
Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 TextsCode0
Multilingual Neural Machine Translation With the Right Amount of Sharing—0
Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology—0
Controlling Extra-Textual Attributes about Dialogue Participants: A Case Study of English-to-Polish Neural Machine Translation—0
A Taxonomy and Study of Critical Errors in Machine Translation—0
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation—0
Fast-Paced Improvements to Named Entity Handling for Neural Machine Translation—0
A Dataset for Term Extraction in Hindi—0
The LTRC Hindi-Telugu Parallel Corpus—0
On the Interaction of Regularization Factors in Low-resource Neural Machine TranslationCode0
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction—0
A Survey of Machine Translation Tasks on Nigerian Languages—0
Construction and Validation of a Japanese Honorific Corpus Based on Systemic Functional Linguistics—0
Constructing Parallel Corpora from COVID-19 News using MediSys Metadata—0
Neural Speech Translation: From Neural Machine Translation to Direct Speech Translation—0
Introducing EM-FT for Manipuri-English Neural Machine Translation—0
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
Connecting client infrastructure with Yamagata Europe machine translation using JSON-based data exchange—0
MT-Pese: Machine Translation and Post-Editese—0
A Machine Translation-Powered Chatbot for Public Administration—0
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine TranslationCode0
Post-editing in Automatic Subtitling: A Subtitlers’ perspectiveCode0
The use of online translators by students not enrolled in a professional translation program: beyond copying and pasting for a professional use—0
A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches—0
Comparing Multilingual NMT Models and Pivoting—0
Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French—0
Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario—0
Achievements of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian—0
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation OutputCode0
Preparing an Endangered Language for the Digital Age: The Case of Judeo-SpanishCode0
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual ModelCode0
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine TranslationCode0
Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning—0
The impact of memory on learning sequence-to-sequence tasksCode0
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers—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