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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 101–150 of 10752 papers

TitleStatusHype
Large Language Models as Span Annotators—0
DeepSeek vs. o3-mini: How Well can Reasoning LLMs Evaluate MT and Summarization?—0
Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question AnsweringCode0
Redefining Machine Translation on Social Network Services with Large Language ModelsCode0
Context-Aware Monolingual Human Evaluation of Machine Translation—0
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-JudgeCode0
DoCIA: An Online Document-Level Context Incorporation Agent for Speech TranslationCode0
M-Prometheus: A Suite of Open Multilingual LLM JudgesCode5
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech TranslationCode2
Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing—0
Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole—0
ContrastScore: Towards Higher Quality, Less Biased, More Efficient Evaluation Metrics with Contrastive Evaluation—0
Overcoming Vocabulary Constraints with Pixel-level Fallback—0
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation—0
Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?—0
You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation—0
The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR—0
Using Source-Side Confidence Estimation for Reliable Translation into Unfamiliar LanguagesCode0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
Non-Monotonic Attention-based Read/Write Policy Learning for Simultaneous Translation—0
Training in translation tools and technologies: Findings of the EMT survey 2023—0
Sociotechnical Effects of Machine Translation—0
HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation—0
Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair—0
Sun-Shine: A Large Language Model for Tibetan CultureCode1
PAD: Towards Efficient Data Generation for Transfer Learning Using Phrase Alignment—0
Natural Language Generation—0
Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future DirectionsCode1
Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation SystemsCode0
Self-Vocabularizing Training for Neural Machine Translation—0
New Trends for Modern Machine Translation with Large Reasoning Models—0
Source-primed Multi-turn Conversation Helps Large Language Models Translate DocumentsCode0
An Expanded Massive Multilingual Dataset for High-Performance Language TechnologiesCode0
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models—0
Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim RetrievalCode0
Explicit Learning and the LLM in Machine TranslationCode0
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems—0
Cross-Lingual IPA Contrastive Learning for Zero-Shot NER—0
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine TranslationCode1
Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms—0
Comparative Study of Zero-Shot Cross-Lingual Transfer for Bodo POS and NER Tagging Using Gemini 2.0 Flash Thinking Experimental Model—0
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine TranslationCode0
Visualising Policy-Reward Interplay to Inform Zeroth-Order Preference Optimisation of Large Language ModelsCode0
Open-Source Large Language Models as Multilingual Crowdworkers: Synthesizing Open-Domain Dialogues in Several Languages With No Examples in Targets and No Machine Translation—0
FourierNAT: A Fourier-Mixing-Based Non-Autoregressive Transformer for Parallel Sequence Generation—0
BatchGEMBA: Token-Efficient Machine Translation Evaluation with Batched Prompting and Prompt CompressionCode0
Wikipedia in the Era of LLMs: Evolution and RisksCode0
QE4PE: Word-level Quality Estimation for Human Post-EditingCode0
Direct Speech to Speech Translation: A Review—0
Co-creation for Sign Language Processing 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