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Found 11 papers, 5 papers with code
HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System
Recently, many task-oriented dialogue systems need to serve users in different languages.
Huawei’s Submissions to the WMT20 Biomedical Translation Task
This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task.
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
Large Language Models (LLMs) are popular for their impressive abilities, but the need for model-specific fine-tuning or task-specific prompt engineering can hinder their generalization.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
Exploring Contextual Relationships for Cervical Abnormal Cell Detection
To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation
Multi-source neural machine translation aims to translate from parallel sources of information (e. g. languages, images, etc.)
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning
A chatbot that converses like a human should be goal-oriented (i. e., be purposeful in conversation), which is beyond language generation.
Applying Cyclical Learning Rate to Neural Machine Translation
In training deep learning networks, the optimizer and related learning rate are often used without much thought or with minimal tuning, even though it is crucial in ensuring a fast convergence to a good quality minimum of the loss function that can also generalize well on the test dataset.
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task
This paper describes Huawei{'}s neural machine translation systems for the WMT 2019 biomedical translation shared task.
Efficient Misalignment-Robust Multi-Focus Microscopical Images Fusion
Our method provides a flexible and efficient way to integrate complementary and redundant information from multiple multi-focus ultra HD unregistered images into a fused image that contains better description than any of the individual input images.
Scale-Invariant Structure Saliency Selection for Fast Image Fusion
Specifically, we propose a scale-invariant structure saliency selection scheme based on the difference-of-Gaussian (DoG) pyramid of images to build the weights or activity map.
