Search Results for author:
Found 9 papers, 4 papers with code
ERNIE: Enhanced Representation through Knowledge Integration
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
Ranked #3 on
Natural Language Inference
on XNLI Chinese Dev
Chinese Named Entity Recognition
Chinese Sentence Pair Classification
+8
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
Ranked #1 on
Chinese Sentence Pair Classification
on LCQMC Dev
Chinese Named Entity Recognition
Chinese Reading Comprehension
+8
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
Ranked #1 on
Question Generation
on SQuAD1.1
(using extra training data)
Pre-trained Language Model for Web-scale Retrieval in Baidu Search
More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.
Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021
This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track.
NVDiff: Graph Generation through the Diffusion of Node Vectors
Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures.
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning
Large pre-trained VLMs like CLIP have demonstrated superior zero-shot recognition ability, and a number of recent studies leverage this ability to mitigate catastrophic forgetting in CL, but they focus on closed-set CL in a single domain dataset.
Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint
Diffusion models have been popular for point cloud generation tasks.
