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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 1–50 of 10752 papers

TitleStatusHype
Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings—0
Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation—0
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation—0
TransLaw: Benchmarking Large Language Models in Multi-Agent Simulation of the Collaborative Translation—0
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval—0
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation—0
Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of ProgressCode0
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical DistillationCode0
Semantic similarity estimation for domain specific data using BERT and other techniques—0
Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search—0
ConsistencyChecker: Tree-based Evaluation of LLM Generalization CapabilitiesCode0
A Gamified Evaluation and Recruitment Platform for Low Resource Language Machine Translation Systems—0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
Gender Bias in English-to-Greek Machine TranslationCode0
TACTIC: Translation Agents with Cognitive-Theoretic Interactive CollaborationCode1
Design of intelligent proofreading system for English translation based on CNN and BERT—0
SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?—0
Prompting LLMs: Length Control for Isometric Machine Translation—0
RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation—0
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT—0
EuroLLM-9B: Technical Report—0
Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons—0
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model TranslationsCode0
VietMix: A Naturally Occurring Vietnamese-English Code-Mixed Corpus with Iterative Augmentation for Machine Translation—0
Limited-Resource Adapters Are Regularizers, Not Linguists—0
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation—0
Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine TranslationCode0
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal TransportCode0
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models—0
Enhancing Large Language Models'Machine Translation via Dynamic Focus Anchoring—0
Translation in the Wild—0
Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreementCode0
Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked DataCode0
Multimodal Machine Translation with Visual Scene Graph Pruning—0
Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative AnalysisCode0
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar—0
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization—0
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine TranslationCode0
UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models—0
Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMsCode1
TULUN: Transparent and Adaptable Low-resource Machine TranslationCode0
Building a Functional Machine Translation Corpus for Kpelle—0
MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis—0
Steering Large Language Models for Machine Translation PersonalizationCode0
Exploring the Relationship Between Diversity and Quality in Ad Text Generation—0
Comparative analysis of subword tokenization approaches for Indian languages—0
HOFT: Householder Orthogonal Fine-tuning—0
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation—0
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based MethodsCode0
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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
5AdminBLEU score43.8—Unverified
6Transformer (ADMIN init)BLEU 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