Search Results for author:
Found 6 papers, 2 papers with code
Exploiting Reasoning Chains for Multi-hop Science Question Answering
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering.
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
Our framework contains three new ideas: (a) {\tt AMR-SG}, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts.
Deep Reinforcement Learning With Adaptive Combined Critics
Then the updated policy is involved in the update of the weight factor, in which we propose a novel method to provide theoretical and experimental guarantee for future policy improvement.
BERTatDE at SemEval-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model
The paper describes our system BERTatDE1 in sentence classification task (subtask 1) and sequence labeling task (subtask 2) in the definition extraction (SemEval-2020 Task 6).
Knowledge Graph Embedding with Atrous Convolution and Residual Learning
To address this issue, we propose a simple but effective atrous convolution based knowledge graph embedding method.
Ranked #1 on
Knowledge Graph Embedding
on FB15k
Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms
These algorithms prove to be far more efficient than their original versions under the framework of deep reinforcement learning, and provide examples for existing and future algorithms to generalize to our new framework.
