Search Results for author:
Found 10 papers, 5 papers with code
An Understanding-Oriented Robust Machine Reading Comprehension Model
In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.
Deep Understanding based Multi-Document Machine Reading Comprehension
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
A Simple but Effective Bidirectional Framework for Relational Triple Extraction
Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods.
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
Next, the mined global associations are integrated into the table feature of each relation.
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.
Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection
Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge selection plays an important role and attracts more and more research interest.
A Conditional Cascade Model for Relational Triple Extraction
Tagging based methods are one of the mainstream methods in relational triple extraction.
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
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
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model
This paper describes our submission to subtask a and b of SemEval-2020 Task 4.
