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Machine Translation
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Context-Aware Monolingual Human Evaluation of Machine Translation
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge
Redefining Machine Translation on Social Network Services with Large Language Models
DeepSeek vs. o3-mini: How Well can Reasoning LLMs Evaluate MT and Summarization?
M-Prometheus: A Suite of Open Multilingual LLM Judges
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation
Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole
Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
ContrastScore: Towards Higher Quality, Less Biased, More Efficient Evaluation Metrics with Contrastive Evaluation
Overcoming Vocabulary Constraints with Pixel-level Fallback
You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation
Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?
Using Source-Side Confidence Estimation for Reliable Translation into Unfamiliar Languages
The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR
Non-Monotonic Attention-based Read/Write Policy Learning for Simultaneous Translation
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation
Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models
Sociotechnical Effects of Machine Translation
Training in translation tools and technologies: Findings of the EMT survey 2023
HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation
Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair
PAD: Towards Efficient Data Generation for Transfer Learning Using Phrase Alignment
Sun-Shine: A Large Language Model for Tibetan Culture
ParsiPy: NLP Toolkit for Historical Persian Texts in Python
Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems
Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions
Natural Language Generation
Self-Vocabularizing Training for Neural Machine Translation
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies
New Trends for Modern Machine Translation with Large Reasoning Models
Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models
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