Papers NMT
“NMT” 태그가 달린 논문 1,772편 · 필터 해제
Asymptotically Smaller Encodings for Graph Problems and Scheduling
We show how several graph problems (e.g., vertex-cover, independent-set, $k$-coloring) can be encoded into CNF using only $O(|V|^2 / \lg |V|)$ many clauses, as opposed to the $\Omega(|V|^2)$ constraints used by standard …
NMTSchedulingConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, w…
Language ModelingLanguage ModellingMachine TranslationNMT+2Enhancing Meme Token Market Transparency: A Multi-Dimensional Entity-Linked Address Analysis for Liquidity Risk Evaluation
Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This pa…
NMTIn-Domain African Languages Translation Using LLMs and Multi-armed Bandits
Neural Machine Translation (NMT) systems face significant challenges when working with low-resource languages, particularly in domain adaptation tasks. These difficulties arise due to limited training data and suboptimal…
Domain AdaptationMachine TranslationModel SelectionMulti-Armed Bandits+3THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation
The sparse Mixture-of-Experts (MoE) has achieved significant progress for neural machine translation (NMT). However, there exist two limitations in current MoE solutions which may lead to sub-optimal performance: 1) they…
Machine TranslationMixture-of-ExpertsNMTTranslationCombining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation
Large language model (LLM) shows promising performances in a variety of downstream tasks, such as machine translation (MT). However, using LLMs for translation suffers from high computational costs and significant latenc…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+4Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
While gender bias in modern Neural Machine Translation (NMT) systems has received much attention, traditional evaluation metrics do not to fully capture the extent to which these systems integrate contextual gender cues.…
Machine TranslationNMTDo Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual Perspective
Current machine translation models provide us with high-quality outputs in most scenarios. However, they still face some specific problems, such as detecting which entities should not be changed during translation. In th…
Machine TranslationNMTTranslationTopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
Most existing multimodal machine translation (MMT) datasets are predominantly composed of static images or short video clips, lacking extensive video data across diverse domains and topics. As a result, they fail to meet…
Domain AdaptationMachine TranslationMultimodal Machine TranslationNMT+1Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda
In this paper,we explore the application of Back translation (BT) as a semi-supervised technique to enhance Neural Machine Translation(NMT) models for the English-Luganda language pair, specifically addressing the challe…
Data AugmentationMachine TranslationNMTTranslationAI 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
Large language models (LLMs) and multi-agent orchestration are touted as the next leap in machine translation (MT), but their benefits relative to conventional neural MT (NMT) remain unclear. This paper offers an empiric…
Machine TranslationNMTCalibrating Translation Decoding with Quality Estimation on LLMs
Neural machine translation (NMT) systems typically employ maximum a posteriori (MAP) decoding to select the highest-scoring translation from the distribution mass. However, recent evidence highlights the inadequacy of MA…
2kMachine TranslationNMTTranslationIs LLM the Silver Bullet to Low-Resource Languages Machine Translation?
Low-Resource Languages (LRLs) present significant challenges in natural language processing due to their limited linguistic resources and underrepresentation in standard datasets. While recent advancements in Large Langu…
ArticlesKnowledge DistillationMachine TranslationNMT+1Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation
Fine-tuning multilingual sequence-to-sequence large language models (msLLMs) has shown promise in developing neural machine translation (NMT) systems for low-resource languages (LRLs). However, conventional single-stage …
Low Resource NMTMachine TranslationNMTTransfer Learning+1Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems
We explore the impact of multi-source input strategies on machine translation (MT) quality, comparing GPT-4o, a large language model (LLM), with a traditional multilingual neural machine translation (NMT) system. Using i…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+2Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
The field of neural machine translation (NMT) has changed with the advent of large language models (LLMs). Much of the recent emphasis in natural language processing (NLP) has been on modeling machine translation and man…
DecoderMachine TranslationNMTTranslationMultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality
Does multilingual Neural Machine Translation (NMT) lead to The Curse of the Multlinguality or provides the Cross-lingual Knowledge Transfer within a language family? In this study, we explore multiple approaches for exte…
Machine TranslationNMTTransfer LearningTranslationQuality-Aware Decoding: Unifying Quality Estimation and Decoding
An emerging research direction in NMT involves the use of Quality Estimation (QE) models, which have demonstrated high correlations with human judgment and can enhance translations through Quality-Aware Decoding. Althoug…
DecoderDocument TranslationNMTRe-Ranking+1Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System
One approach for multilingual data-to-text generation is to translate grammatical configurations upfront from the source language into each target language. These configurations are then used by a surface realizer and in…
Data-to-Text GenerationMachine TranslationNMTText Generation+1Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction
Ensembling neural machine translation (NMT) models to produce higher-quality translations than the $L$ individual models has been extensively studied. Recent methods typically employ a candidate selection block (CSB) and…
DecoderMachine TranslationNMTreinforcement-learning+4