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

Showing 58515900 of 10752 papers

TitleStatusHype
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation0
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation0
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation0
Offline Sentence Processing Measures for testing Readability with Users0
Off-the-Shelf Unsupervised NMT0
Okapi+QuEst: Translation Quality Estimation within Okapi0
OkwuGbé: End-to-End Speech Recognition for Fon and Igbo0
Omnifluent English-to-French and Russian-to-English Systems for the 2013 Workshop on Statistical Machine Translation0
Omorfi --- Free and open source morphological lexical database for Finnish0
OMWEdit - The Integrated Open Multilingual Wordnet Editing System0
On a novel training algorithm for sequence-to-sequence predictive recurrent networks0
On A Strictly Convex IBM Model 10
On Complex Word Alignment Configurations0
On Compositionality in Neural Machine Translation0
On Context Span Needed for Machine Translation Evaluation0
On Controllable Sparse Alternatives to Softmax0
On Decoding Strategies for Neural Text Generators0
On-Demand Distributional Semantic Distance and Paraphrasing0
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval0
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval0
``One Entity per Discourse'' and ``One Entity per Collocation'' Improve Named-Entity Disambiguation0
InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model0
One Model to Learn Both: Zero Pronoun Prediction and Translation0
One Sentence One Model for Neural Machine Translation0
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities0
One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages0
One Step Closer to Automatic Evaluation of Text Simplification Systems0
One-stop Training of Multiple Capacity Models0
One Tense per Scene: Predicting Tense in Chinese Conversations0
One-To-Many Multilingual End-to-end Speech Translation0
One Tree is not Enough: Cross-lingual Accumulative Structure Transfer for Semantic Indeterminacy0
On Extending Direct Preference Optimization to Accommodate Ties0
Ongoing Study for Enhancing Chinese-Spanish Translation with Morphology Strategies0
On Hierarchical Re-ordering and Permutation Parsing for Phrase-based Decoding0
On “Human Parity” and “Super Human Performance” in Machine Translation Evaluation0
On Improving the Human Translation Process by Using MT Technologies under a Cognitive Framework0
On Instruction-Finetuning Neural Machine Translation Models0
On Integrating Discourse in Machine Translation0
On Knowledge Distillation for Direct Speech Translation0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions0
On Learning Language-Invariant Representations for Universal Machine Translation0
On Learning Meaningful Code Changes via Neural Machine Translation0
On Learning Universal Representations Across Languages0
On Leveraging the Visual Modality for Neural Machine Translation0
Online Automatic Post-editing for MT in a Multi-Domain Translation Environment0
Online Distilling from Checkpoints for Neural Machine Translation0
Online Learning Approaches in Computer Assisted Translation0
Online Learning for Effort Reduction in Interactive Neural Machine Translation0
Online Learning for Neural Machine Translation Post-editing0
Online Learning for Statistical Machine Translation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer Cycle (Rev)BLEU score35.14Unverified
2Noisy back-translationBLEU score35Unverified
3Transformer+Rep(Uni)BLEU score33.89Unverified
4T5-11BBLEU score32.1Unverified
5BiBERTBLEU score31.26Unverified
6Transformer + R-DropBLEU score30.91Unverified
7Bi-SimCutBLEU score30.78Unverified
8BERT-fused NMTBLEU score30.75Unverified
9Data Diversification - TransformerBLEU score30.7Unverified
10SimCutBLEU score30.56Unverified
#ModelMetricClaimedVerifiedStatus
1Transformer+BT (ADMIN init)BLEU score46.4Unverified
2Noisy back-translationBLEU score45.6Unverified
3mRASP+Fine-TuneBLEU score44.3Unverified
4Transformer + R-DropBLEU score43.95Unverified
5Transformer (ADMIN init)BLEU score43.8Unverified
6AdminBLEU score43.8Unverified
7BERT-fused NMTBLEU score43.78Unverified
8MUSE(Paralllel Multi-scale Attention)BLEU score43.5Unverified
9T5BLEU score43.4Unverified
10Local Joint Self-attentionBLEU score43.3Unverified
#ModelMetricClaimedVerifiedStatus
1PiNMTBLEU score40.43Unverified
2BiBERTBLEU score38.61Unverified
3Bi-SimCutBLEU score38.37Unverified
4Cutoff + Relaxed Attention + LMBLEU score37.96Unverified
5DRDABLEU score37.95Unverified
6Transformer + R-Drop + CutoffBLEU score37.9Unverified
7SimCutBLEU score37.81Unverified
8Cutoff+KneeBLEU score37.78Unverified
9CutoffBLEU score37.6Unverified
10CipherDAugBLEU score37.53Unverified
#ModelMetricClaimedVerifiedStatus
1HWTSC-Teacher-SimScore19.97Unverified
2MS-COMET-22Score19.89Unverified
3MS-COMET-QE-22Score19.76Unverified
4KG-BERTScoreScore17.28Unverified
5metricx_xl_DA_2019Score17.17Unverified
6COMET-QEScore16.8Unverified
7COMET-22Score16.31Unverified
8UniTE-srcScore15.68Unverified
9UniTE-refScore15.38Unverified
10metricx_xxl_DA_2019Score15.24Unverified