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.
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.
Next, the mined global associations are integrated into the table feature of each relation.
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.
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.
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.
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
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).
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
This paper describes our submission to subtask a and b of SemEval-2020 Task 4.
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.
In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented.
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.