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 1401–1450 of 10752 papers

TitleStatusHype
Towards Multilingual Audio-Visual Question AnsweringCode0
Improving Autoregressive Training with Dynamic Oracles—0
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine TranslationCode0
Guiding In-Context Learning of LLMs through Quality Estimation for Machine TranslationCode0
Languages Transferred Within the Encoder: On Representation Transfer in Zero-Shot Multilingual TranslationCode0
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language ModelCode0
Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning—0
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback MechanismsCode0
On the Hallucination in Simultaneous Machine TranslationCode0
Textual Similarity as a Key Metric in Machine Translation Quality Estimation—0
Symmetric Dot-Product Attention for Efficient Training of BERT Language Models—0
Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into GermanCode0
Efficient k-Nearest-Neighbor Machine Translation with Dynamic RetrievalCode0
Feriji: A French-Zarma Parallel Corpus, Glossary & Translator—0
Do LLMs Recognize me, When I is not me: Assessment of LLMs Understanding of Turkish Indexical Pronouns in Indexical Shift Contexts—0
On Subjective Uncertainty Quantification and Calibration in Natural Language GenerationCode0
Recovering document annotations for sentence-level bitext—0
How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages?Code0
Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation—0
LCS: A Language Converter Strategy for Zero-Shot Neural Machine TranslationCode0
What is the Best Way for ChatGPT to Translate Poetry?—0
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors DetectionCode0
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering—0
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation—0
Deterministic Reversible Data Augmentation for Neural Machine TranslationCode0
Self-Modifying State Modeling for Simultaneous Machine TranslationCode0
LexMatcher: Dictionary-centric Data Collection for LLM-based Machine TranslationCode0
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation—0
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM AbilitiesCode0
Significance of Chain of Thought in Gender Bias Mitigation for English-Dravidian Machine Translation—0
How Multilingual Are Large Language Models Fine-Tuned for Translation?—0
Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning—0
Integrating Multi-scale Contextualized Information for Byte-based Neural Machine TranslationCode0
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective—0
On the Sequence Evaluation based on Stochastic Processes—0
The Multi-Range Theory of Translation Quality Measurement: MQM scoring models and Statistical Quality Control—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information—0
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks—0
Exploring Alignment in Shared Cross-lingual SpacesCode0
Optimizing example selection for retrieval-augmented machine translation with translation memories—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
A Survey on Multi-modal Machine Translation: Tasks, Methods and Challenges—0
FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation PurposesCode0
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference CorpusCode0
Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving Machine Translation—0
Beyond MLE: Investigating SEARNN for Low-Resourced Neural Machine Translation—0
Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study—0
Word Alignment as Preference for Machine Translation—0
LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages—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