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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.
Aya Vision: Advancing the Frontier of Multilingual Multimodality
Bridging the Gap: An Intermediate Language for Enhanced and Cost-Effective Grapheme-to-Phoneme Conversion with Homographs with Multiple Pronunciations Disambiguation
Development of a WAZOBIA-Named Entity Recognition System
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
Do Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual Perspective
LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answering
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda
Proper Name Diacritization for Arabic Wikipedia: A Benchmark Dataset
SignSplat: Rendering Sign Language via Gaussian Splatting
Same evaluation, more tokens: On the effect of input length for machine translation evaluation using Large Language Models
AI agents may be worth the hype but not the resources (yet): An initial exploration of machine translation quality and costs in three language pairs in the legal and news domains
Sadeed: Advancing Arabic Diacritization Through Small Language Model
Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation
Team ACK at SemEval-2025 Task 2: Beyond Word-for-Word Machine Translation for English-Korean Pairs
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?
Calibrating Translation Decoding with Quality Estimation on LLMs
FairTranslate: An English-French Dataset for Gender Bias Evaluation in Machine Translation by Overcoming Gender Binarity
CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents
Automatic Evaluation Metrics for Document-level Translation: Overview, Challenges and Trends
Translation Analytics for Freelancers: I. Introduction, Data Preparation, Baseline Evaluations
Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data
A Multimodal Recaptioning Framework to Account for Perceptual Diversity in Multilingual Vision-Language Modeling
Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling
Are AI agents the new machine translation frontier? Challenges and opportunities of single- and multi-agent systems for multilingual digital communication
Déjà Vu: Multilingual LLM Evaluation through the Lens of Machine Translation Evaluation
ADAT: Time-Series-Aware Adaptive Transformer Architecture for Sign Language Translation
Multilingual Contextualization of Large Language Models for Document-Level Machine Translation
Automated Python Translation
AskQE: Question Answering as Automatic Evaluation for Machine Translation
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning
MorphTok: Morphologically Grounded Tokenization for Indian Languages
Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs
LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline
Large Language Models as Span Annotators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.