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Found 18 papers, 4 papers with code
X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale
In this paper, we prioritize quality over scaling number of languages, with a focus on multilingual machine translation task, and introduce X-ALMA, a model designed with a commitment to ensuring top-tier performance across 50 diverse languages, regardless of their resource levels.
On-the-Fly Fusion of Large Language Models and Machine Translation
We propose the on-the-fly ensembling of a machine translation model with an LLM, prompted on the same task and input.
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications
To address such limitations, meta-learning has been adopted in the scenarios of few-shot learning and multiple tasks.
GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention
Single Image Super-Resolution (SISR) is a very active research field.
ParaCrawl: Web-Scale Acquisition of Parallel Corpora
We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software.
Marian: Cost-effective High-Quality Neural Machine Translation in C++
This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task.
Fast Neural Machine Translation Implementation
This paper describes the submissions to the efficiency track for GPUs at the Workshop for Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz University, Tilde and University of Alicante.
Exploring Hyper-Parameter Optimization for Neural Machine Translation on GPU Architectures
Neural machine translation (NMT) has been accelerated by deep learning neural networks over statistical-based approaches, due to the plethora and programmability of commodity heterogeneous computing architectures such as FPGAs and GPUs and the massive amount of training corpuses generated from news outlets, government agencies and social media.
Marian: Fast Neural Machine Translation in C++
We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs.
A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages
We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic.
Fast, Scalable Phrase-Based SMT Decoding
The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community.
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions.
