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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 1–50 of 1773 papers

TitleStatusHype
Asymptotically Smaller Encodings for Graph Problems and Scheduling—0
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT—0
Enhancing Meme Token Market Transparency: A Multi-Dimensional Entity-Linked Address Analysis for Liquidity Risk Evaluation—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation—0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation—0
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine TranslationCode0
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for DocumentariesCode0
Do Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual PerspectiveCode0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
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—0
Calibrating Translation Decoding with Quality Estimation on LLMsCode0
Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?—0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems—0
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine TranslationCode1
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality—0
Quality-Aware Decoding: Unifying Quality Estimation and Decoding—0
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System—0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
AFRIDOC-MT: Document-level MT Corpus for African LanguagesCode0
Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models—0
Quality Estimation based Feedback Training for Improving Pronoun Translation—0
Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq ApproachesCode0
Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs—0
Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation—0
Advancing Explainability in Neural Machine Translation: Analytical Metrics for Attention and Alignment Consistency—0
An Analysis on Automated Metrics for Evaluating Japanese-English Chat Translation—0
Domain adapted machine translation: What does catastrophic forgetting forget and why?—0
Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair—0
Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray—0
ParMod: A Parallel and Modular Framework for Learning Non-Markovian Tasks—0
MT-LENS: An all-in-one Toolkit for Better Machine Translation EvaluationCode1
A Comparative Study of LLMs, NMT Models, and Their Combination in Persian-English Idiom TranslationCode0
Shiksha: A Technical Domain focused Translation Dataset and Model for Indian Languages—0
Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation—0
A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and SinhalaCode0
From Priest to Doctor: Domain Adaptaion for Low-Resource Neural Machine TranslationCode0
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering—0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial ApproachCode0
On Creating an English-Thai Code-switched Machine Translation in Medical DomainCode0
Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?—0
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset RepositoryCode0
QE-EBM: Using Quality Estimators as Energy Loss for Machine Translation—0
NusaMT-7B: Machine Translation for Low-Resource Indonesian Languages with Large Language Models—0
Optimizing the Training Schedule of Multilingual NMT using Reinforcement LearningCode0
On Instruction-Finetuning Neural Machine Translation Models—0
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