Search Results for author: Weihua Peng

Found 16 papers, 4 papers with code

Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text

no code implementations EMNLP 2020 Dongfang Li, Baotian Hu, Qingcai Chen, Weihua Peng, Anqi Wang

Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.

Machine Reading Comprehension

Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications

no code implementations10 Nov 2023 Zhangyin Feng, Weitao Ma, Weijiang Yu, Lei Huang, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting Liu

In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.

knowledge editing Retrieval

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

1 code implementation9 Nov 2023 Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting Liu

The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation.


A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

1 code implementation27 Sep 2023 Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu

Chain-of-thought reasoning, a cognitive process fundamental to human intelligence, has garnered significant attention in the realm of artificial intelligence and natural language processing.

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning

no code implementations18 Oct 2022 Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng

This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps.


Mixture of Experts for Biomedical Question Answering

no code implementations15 Apr 2022 Damai Dai, Wenbin Jiang, Jiyuan Zhang, Weihua Peng, Yajuan Lyu, Zhifang Sui, Baobao Chang, Yong Zhu

In this paper, in order to alleviate the parameter competition problem, we propose a Mixture-of-Expert (MoE) based question answering method called MoEBQA that decouples the computation for different types of questions by sparse routing.

Question Answering

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

1 code implementation ACL 2022 Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng

Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.

Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks

no code implementations ACL 2021 Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, Yuguang Chen, Weihua Peng

Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.

Descriptive Event Causality Identification

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

no code implementations ACL 2021 Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences.

Data Augmentation Event Causality Identification

MedWriter: Knowledge-Aware Medical Text Generation

no code implementations COLING 2020 Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou

To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical.

Text Generation

Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks

no code implementations9 May 2020 Junheng Huang, Lu Pan, Kang Xu, Weihua Peng, Fayuan Li

In this paper, we propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN), which can utilize the topic information hidden in the articles to guide the generation of pertinent and diversified comments.

Comment Generation Text Generation

Neural Data-to-Text Generation with Dynamic Content Planning

no code implementations16 Apr 2020 Kai Chen, Fayuan Li, Baotian Hu, Weihua Peng, Qingcai Chen, Hong Yu

We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text.

Data-to-Text Generation

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