Search Results for author: Feiliang Ren

Found 17 papers, 5 papers with code

Deep Understanding based Multi-Document Machine Reading Comprehension

no code implementations25 Feb 2022 Feiliang Ren, Yongkang Liu, Bochao Li, Zhibo Wang, Yu Guo, Shilei Liu, Huimin Wu, Jiaqi Wang, Chunchao Liu, Bingchao Wang

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.

Machine Reading Comprehension

A Simple but Effective Bidirectional Framework for Relational Triple Extraction

1 code implementation9 Dec 2021 Feiliang Ren, Longhui Zhang, Xiaofeng Zhao, Shujuan Yin, Shilei Liu, Bochao Li

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 Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation

1 code implementation EMNLP 2021 Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren, Longhui Zhang, Shujuan Yin

Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.

Dialogue Generation Response Generation

Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection

no code implementations31 Aug 2021 Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren

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.

Response Generation

A Conditional Cascade Model for Relational Triple Extraction

1 code implementation20 Aug 2021 Feiliang Ren, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Shilei Liu, Bochao Li

Tagging based methods are one of the mainstream methods in relational triple extraction.

An Effective System for Multi-format Information Extraction

1 code implementation16 Aug 2021 Yaduo Liu, Longhui Zhang, Shujuan Yin, Xiaofeng Zhao, Feiliang Ren

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.

Document-level Event Extraction Multi-Task Learning +2

BERTatDE at SemEval-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model

no code implementations SEMEVAL 2020 Huihui Zhang, Feiliang Ren

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).

Definition Extraction Sentence Classification

Domain Representation for Knowledge Graph Embedding

no code implementations26 Mar 2019 Cunxiang Wang, Feiliang Ren, Zhichao Lin, Chenxv Zhao, Tian Xie, Yue Zhang

Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.

Knowledge Graph Embedding Link Prediction

Neural Relation Classification with Text Descriptions

no code implementations COLING 2018 Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, Xiaobo Liang

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.

Classification General Classification +1

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