Search Results for author: Yinghui Li

Found 9 papers, 2 papers with code

Automatic Context Pattern Generation for Entity Set Expansion

no code implementations17 Jul 2022 Yinghui Li, Shulin Huang, Xinwei Zhang, Qingyu Zhou, Yangning Li, Ruiyang Liu, Yunbo Cao, Hai-Tao Zheng, Ying Shen

A non-negligible shortcoming of the pre-defined context patterns is that they cannot be flexibly generalized to all kinds of semantic classes, and we call this phenomenon as "semantic sensitivity".

Contrastive Learning with Hard Negative Entities for Entity Set Expansion

1 code implementation16 Apr 2022 Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen, Hai-Tao Zheng

In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities.

Contrastive Learning Language Modelling

A Survey of Natural Language Generation

no code implementations22 Dec 2021 Chenhe Dong, Yinghui Li, Haifan Gong, Miaoxin Chen, Junxin Li, Ying Shen, Min Yang

This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology.

Data-to-Text Generation

Are we ready for a new paradigm shift? A Survey on Visual Deep MLP

1 code implementation7 Nov 2021 Ruiyang Liu, Yinghui Li, Linmi Tao, Dun Liang, Hai-Tao Zheng

In the GPU era, the locally and globally weighted summations are the current mainstreams, represented by the convolution and self-attention mechanism, as well as MLP.

A non-hierarchical attention network with modality dropout for textual response generation in multimodal dialogue systems

no code implementations19 Oct 2021 Rongyi Sun, Borun Chen, Qingyu Zhou, Yinghui Li, Yunbo Cao, Hai-Tao Zheng

Existing text- and image-based multimodal dialogue systems use the traditional Hierarchical Recurrent Encoder-Decoder (HRED) framework, which has an utterance-level encoder to model utterance representation and a context-level encoder to model context representation.

Response Generation

Learning Purified Feature Representations from Task-irrelevant Labels

no code implementations22 Feb 2021 Yinghui Li, Chen Wang, Yangning Li, Hai-Tao Zheng, Ying Shen

Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks.

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