Search Results for author:
Found 6 papers, 3 papers with code
Coreferential Reasoning Learning for Language Representation
Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning.
Ranked #31 on
Relation Extraction
on DocRED
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
Human conversations naturally evolve around related concepts and scatter to multi-hop concepts.
Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network
Multi-paragraph reasoning is indispensable for open-domain question answering (OpenQA), which receives less attention in the current OpenQA systems.
Ranked #58 on
Question Answering
on HotpotQA
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs.
Ranked #59 on
Relation Extraction
on DocRED
Understanding the Behaviors of BERT in Ranking
This paper studies the performances and behaviors of BERT in ranking tasks.
Deep Hybrid Scattering Image Learning
A well-trained deep neural network is shown to gain capability of simultaneously restoring two kinds of images, which are completely destroyed by two distinct scattering medias respectively.
