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Found 15 papers, 8 papers with code
Phrase-aware Unsupervised Constituency Parsing
Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.
CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion
Recent advancements in text-to-image generative systems have been largely driven by diffusion models.
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Recently years have witnessed a rapid development of large language models (LLMs).
Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models
In this paper, we start with a conceptual definition of AGI and briefly review how NLP solves a wide range of tasks via a chat system.
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism
The Mixture of Experts (MoE) model becomes an important choice of large language models nowadays because of its scalability with sublinear computational complexity for training and inference.
Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast.
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names.
Ranked #1 on
Phrase Tagging
on KPTimes
On the Transformer Growth for Progressive BERT Training
Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressively -- start from an inferior but low-cost model and gradually grow the model to increase the computational complexity.
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions.
Generating Representative Headlines for News Stories
In this work, we study the problem of generating representative headlines for news stories.
Learning Dynamic Context Augmentation for Global Entity Linking
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space.
Ranked #5 on
Entity Disambiguation
on AIDA-CoNLL
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering
Recently, distant supervision has gained great success on Fine-grained Entity Typing (FET).
Learning Named Entity Tagger using Domain-Specific Dictionary
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features.
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms.
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news.
