Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Ye, Deming"'
Autor:
Zhang, Zhengyan, Zeng, Zhiyuan, Lin, Yankai, Wang, Huadong, Ye, Deming, Xiao, Chaojun, Han, Xu, Liu, Zhiyuan, Li, Peng, Sun, Maosong, Zhou, Jie
Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy new knowledge injection methods or knowledge bases for downstream tasks.
Externí odkaz:
http://arxiv.org/abs/2305.17691
Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge injection methods are either applicable to structured knowledge or unstruc
Externí odkaz:
http://arxiv.org/abs/2305.01624
Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose to build a simple but effective Pluggable Entity Lookup Table (PELT)
Externí odkaz:
http://arxiv.org/abs/2202.13392
Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In
Externí odkaz:
http://arxiv.org/abs/2109.06067
Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To address this issue, we propose a dynamic token reduction approach to acceler
Externí odkaz:
http://arxiv.org/abs/2105.11618
Autor:
Zhang, Zhengyan, Han, Xu, Zhou, Hao, Ke, Pei, Gu, Yuxian, Ye, Deming, Qin, Yujia, Su, Yusheng, Ji, Haozhe, Guan, Jian, Qi, Fanchao, Wang, Xiaozhi, Zheng, Yanan, Zeng, Guoyang, Cao, Huanqi, Chen, Shengqi, Li, Daixuan, Sun, Zhenbo, Liu, Zhiyuan, Huang, Minlie, Han, Wentao, Tang, Jie, Li, Juanzi, Zhu, Xiaoyan, Sun, Maosong
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning.
Externí odkaz:
http://arxiv.org/abs/2012.00413
Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning. However, most exist
Externí odkaz:
http://arxiv.org/abs/2004.06870
Multi-paragraph reasoning is indispensable for open-domain question answering (OpenQA), which receives less attention in the current OpenQA systems. In this work, we propose a knowledge-enhanced graph neural network (KGNN), which performs reasoning o
Externí odkaz:
http://arxiv.org/abs/1911.02170
Autor:
Yao, Yuan, Ye, Deming, Li, Peng, Han, Xu, Lin, Yankai, Liu, Zhenghao, Liu, Zhiyuan, Huang, Lixin, Zhou, Jie, Sun, Maosong
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order
Externí odkaz:
http://arxiv.org/abs/1906.06127
RNNs and their variants have been widely adopted for image captioning. In RNNs, the production of a caption is driven by a sequence of latent states. Existing captioning models usually represent latent states as vectors, taking this practice for gran
Externí odkaz:
http://arxiv.org/abs/1807.09958