Search Results for author:
Found 10 papers, 7 papers with code
MC-BERT: Efficient Language Pre-Training via a Meta Controller
Pre-trained contextual representations (e. g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks.
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information.
Ranked #3 on
Link Prediction
on UMLS
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature.
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context
Creating effective visualization is an important part of data analytics.
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers
This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5.
Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task.
Ranked #1 on
Code Completion
on SAFIM
What Does a TextCNN Learn?
TextCNN, the convolutional neural network for text, is a useful deep learning algorithm for sentence classification tasks such as sentiment analysis and question classification.
Microsoft Research Asia's Systems for WMT19
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks.
ADELT: Transpilation Between Deep Learning Frameworks
We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks.
