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Found 19 papers, 7 papers with code
TechGPT-2.0: A large language model project to solve the task of knowledge graph construction
Large language models have exhibited robust performance across diverse natural language processing tasks.
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.
An Effective System for Multi-format Information Extraction
Finally, our system ranks No. 4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79. 887%, 85. 179%, and 70. 828% respectively.
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
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
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.
Domain Representation for Knowledge Graph Embedding
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.
TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph
In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented.
Neural Relation Classification with Text Descriptions
State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given.
