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 9101–9150 of 10752 papers

TitleStatusHype
Collocation or Free Combination? --- Applying Machine Translation Techniques to identify collocations in Japanese—0
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging—0
Combination of Neural Machine Translation Systems at WMT20—0
Combination of Statistical and Neural Machine Translation for Myanmar-English—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Combining Bilingual and Comparable Corpora for Low Resource Machine Translation—0
Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention—0
Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation—0
Combining Compositionality and Pagerank for the Identification of Semantic Relations between Biomedical Words—0
Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages—0
Combining Domain Adaptation Approaches for Medical Text Translation—0
Combining EBMT, SMT, TM and IR Technologies for Quality and Scale—0
Combining fast\_align with Hierarchical Sub-sentential Alignment for Better Word Alignments—0
Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training—0
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums—0
Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19—0
Combining Multiple Alignments to Improve Machine Translation—0
Combining PBSMT and NMT Back-translated Data for Efficient NMT—0
Combining Phonology and Morphology for the Normalization of Historical Texts—0
Combining Punctuation and Disfluency Prediction: An Empirical Study—0
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output—0
Combining Quality Prediction and System Selection for Improved Automatic Translation Output—0
Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling—0
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models—0
Combining SMT and NMT Back-Translated Data for Efficient NMT—0
Combining Statistical Machine Translation and Translation Memories with Domain Adaptation—0
Combining Statistical Translation Techniques for Cross-Language Information Retrieval—0
Combining String and Context Similarity for Bilingual Term Alignment from Comparable Corpora—0
Combining Subword Representations into Word-level Representations in the Transformer Architecture—0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation—0
Combining the output of two coreference resolution systems for two source languages to improve annotation projection—0
Combining Top-down and Bottom-up Search for Unsupervised Induction of Transduction Grammars—0
Combining Translation Memories and Syntax-Based SMT: Experiments with Real Industrial Data—0
Combining Translation Memory with Neural Machine Translation—0
Combining Word and Character Vector Representation on Neural Machine Translation—0
Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages—0
Combining Word Reordering Methods on different Linguistic Abstraction Levels for Statistical Machine Translation—0
CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models—0
COMET: A Neural Framework for MT Evaluation—0
COMET-QE and Active Learning for Low-Resource Machine Translation—0
Common Round: Application of Language Technologies to Large-Scale Web Debates—0
Communication-Efficient Federated Learning for Neural Machine Translation—0
Compact and Robust Models for Japanese-English Character-level Machine Translation—0
Compact, Efficient and Unlimited Capacity: Language Modeling with Compressed Suffix Trees—0
Compact Personalized Models for Neural Machine Translation—0
Comparative Analysis of CHATGPT and the evolution of language models—0
Comparative analysis of subword tokenization approaches for Indian languages—0
Comparative Quality Estimation: Automatic Sentence-Level Ranking of Multiple Machine Translation Outputs—0
Comparative Study of Zero-Shot Cross-Lingual Transfer for Bodo POS and NER Tagging Using Gemini 2.0 Flash Thinking Experimental Model—0
Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation—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