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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.
Multimodal Machine Translation with Visual Scene Graph Pruning
Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar
Evaluating Machine Translation Models for English-Hindi Language Pairs: A Comparative Analysis
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization
MT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation
UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models
Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs
TULUN: Transparent and Adaptable Low-resource Machine Translation
Building a Functional Machine Translation Corpus for Kpelle
MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
Memorization or Reasoning? Exploring the Idiom Understanding of LLMs
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation
Exploring the Relationship Between Diversity and Quality in Ad Text Generation
HOFT: Householder Orthogonal Fine-tuning
Steering Large Language Models for Machine Translation Personalization
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation
Comparative analysis of subword tokenization approaches for Indian languages
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
Exploring In-Image Machine Translation with Real-World Background
THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation
TransBench: Benchmarking Machine Translation for Industrial-Scale Applications
FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing
Scaling Low-Resource MT via Synthetic Data Generation with LLMs
Pivot Language for Low-Resource Machine Translation
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement Learning
A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation
LLM-Based Evaluation of Low-Resource Machine Translation: A Reference-less Dialect Guided Approach with a Refined Sylheti-English Benchmark
Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model
Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits
Lost in Transliteration: Bridging the Script Gap in Neural IR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.