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Found 9 papers, 2 papers with code
Vertical Semi-Federated Learning for Efficient Online Advertising
Specifically, we build an inference-efficient single-party student model applicable to the whole sample space and meanwhile maintain the advantage of the federated feature extension.
VFed-SSD: Towards Practical Vertical Federated Advertising
As an emerging secure learning paradigm in lever-aging cross-agency private data, vertical federatedlearning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher.
XGPT: Cross-modal Generative Pre-Training for Image Captioning
While many BERT-based cross-modal pre-trained models produce excellent results on downstream understanding tasks like image-text retrieval and VQA, they cannot be applied to generation tasks directly.
Multi-View Learning for Vision-and-Language Navigation
Learning to navigate in a visual environment following natural language instructions is a challenging task because natural language instructions are highly variable, ambiguous, and under-specified.
Robust Navigation with Language Pretraining and Stochastic Sampling
Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments.
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention
The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context.
Incorporating Glosses into Neural Word Sense Disambiguation
GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.
Ranked #3 on
Word Sense Disambiguation
on SemEval 2015 Task 13
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data
But the training data of single corpus is often limited.
Improving Chinese SRL with Heterogeneous Annotations
Previous studies on Chinese semantic role labeling (SRL) have concentrated on single semantically annotated corpus.
