Search Results for author:
Found 14 papers, 10 papers with code
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
Event Time Extraction and Propagation via Graph Attention Networks
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks.
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy.
A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
Adversarial training has been shown effective at endowing the learned representations with stronger generalization ability.
Ranked #7 on
Machine Translation
on IWSLT2014 German-English
GIKT: A Graph-based Interaction Model for Knowledge Tracing
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.
Ranked #7 on
Knowledge Tracing
on EdNet
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problems.
Ranked #1 on
Click-Through Rate Prediction
on MovieLens 1M
Dynamically Fused Graph Network for Multi-hop Reasoning
However, many difficult questions require multiple supporting evidence from scattered text among two or more documents.
Ranked #35 on
Question Answering
on HotpotQA
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance.
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.
Medical Named Entity Recognition
named-entity-recognition
+3
QA4IE: A Question Answering based Framework for Information Extraction
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts.
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
Ranked #1 on
Click-Through Rate Prediction
on iPinYou
