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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 26–50 of 1773 papers

TitleStatusHype
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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