research
∙
09/20/2023
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
Generative Large Language Models (LLMs) have achieved remarkable advance...
share
research
∙
05/24/2023
Leveraging GPT-4 for Automatic Translation Post-Editing
While Neural Machine Translation (NMT) represents the leading approach t...
research
∙
02/18/2023
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
Generative Pre-trained Transformer (GPT) models have shown remarkable ca...
research
∙
10/21/2021
On Hard Episodes in Meta-Learning
Existing meta-learners primarily focus on improving the average task acc...
research
∙
09/17/2021
Semi-Supervised Few-Shot Intent Classification and Slot Filling
Intent classification (IC) and slot filling (SF) are two fundamental tas...
research
∙
10/14/2020
Data Augmentation for Meta-Learning
Conventional image classifiers are trained by randomly sampling mini-bat...
research
∙
10/13/2020
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Generative models are increasingly able to produce remarkably high quali...
research
∙
05/26/2020
Active Imitation Learning with Noisy Guidance
Imitation learning algorithms provide state-of-the-art results on many s...
research
∙
04/06/2020
Meta-Learning for Few-Shot NMT Adaptation
We present META-MT, a meta-learning approach to adapt Neural Machine Tra...
research
∙
01/23/2019
Meta-Learning for Contextual Bandit Exploration
We describe MELEE, a meta-learning algorithm for learning a good explora...
research
∙
11/27/2018
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
In the slot-filling paradigm, where a user can refer back to slots in th...
research
∙
08/03/2017
