Search Results for author: Yinghui Li

Found 40 papers, 16 papers with code

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.

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction

2 code implementations19 Oct 2022 Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen

Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.

Grammatical Error Correction

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

MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities

1 code implementation27 Jul 2023 Yangning Li, Tingwei Lu, Yinghui Li, Tianyu Yu, Shulin Huang, Hai-Tao Zheng, Rui Zhang, Jun Yuan

The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class.

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check

1 code implementation13 Oct 2023 Haojing Huang, Jingheng Ye, Qingyu Zhou, Yinghui Li, Yangning Li, Feng Zhou, Hai-Tao Zheng

In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task in an end-to-end fashion.

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark

1 code implementation29 Feb 2024 Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Hai-Tao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang

To promote the improvement of Chinese LLMs' ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet.

Question Answering

CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction

2 code implementations18 May 2023 Jingheng Ye, Yinghui Li, Qingyu Zhou, Yangning Li, Shirong Ma, Hai-Tao Zheng, Ying Shen

Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity.

Grammatical Error Correction

Automatic Context Pattern Generation for Entity Set Expansion

1 code implementation17 Jul 2022 Yinghui Li, Shulin Huang, Xinwei Zhang, Qingyu Zhou, Yangning Li, Ruiyang Liu, Yunbo Cao, Hai-Tao Zheng, Ying Shen

In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.

Information Retrieval Retrieval +1

Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation

1 code implementation29 Oct 2022 Shulin Huang, Shirong Ma, Yinghui Li, Yangning Li, Shiyang Lin, Hai-Tao Zheng, Ying Shen

Facing this dilemma, we focus on a novel CTG scenario, i. e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e. g., objects and occasions).

Attribute Text Generation

LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles

1 code implementation21 Aug 2023 Shulin Huang, Shirong Ma, Yinghui Li, Mengzuo Huang, Wuhe Zou, Weidong Zhang, Hai-Tao Zheng

With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities.

Logical Reasoning

UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities

1 code implementation7 Mar 2024 Yangning Li, Qingsong Lv, Tianyu Yu, Yinghui Li, Shulin Huang, Tingwei Lu, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Hui Wang

To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes.

Attribute Contrastive Learning +1

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.

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

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 nlg evaluation

Focus Is What You Need For Chinese Grammatical Error Correction

no code implementations23 Oct 2022 Jingheng Ye, Yinghui Li, Shirong Ma, Rui Xie, Wei Wu, Hai-Tao Zheng

Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text.

Grammatical Error Correction Sentence

A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking

no code implementations27 Oct 2022 Zilin Yuan, Yinghui Li, Yangning Li, Rui Xie, Wei Wu, Hai-Tao Zheng

We noted that the distinctness of the domain-specific features is different, so in this paper, we propose to use a curriculum learning strategy based on keyword weight ranking to improve the performance of multi-domain text classification models.

text-classification Text Classification

Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge

no code implementations20 Nov 2022 Yangning Li, Jiaoyan Chen, Yinghui Li, Tianyu Yu, Xi Chen, Hai-Tao Zheng

Extensive experiments demonstrate that PICSO can dramatically outperform the original PLMs and the other knowledge and synonym injection models on four different similarity-oriented tasks.

Entity Linking Language Modelling +4

Knowledge-augmented Few-shot Visual Relation Detection

no code implementations9 Mar 2023 Tianyu Yu, Yangning Li, Jiaoyan Chen, Yinghui Li, Hai-Tao Zheng, Xi Chen, Qingbin Liu, Wenqiang Liu, Dongxiao Huang, Bei Wu, Yexin Wang

Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD.

Few-Shot Learning Language Modelling +1

From Retrieval to Generation: Efficient and Effective Entity Set Expansion

no code implementations7 Apr 2023 Shulin Huang, Shirong Ma, Yangning Li, Yinghui Li, Yong Jiang, Hai-Tao Zheng, Ying Shen

For efficiency, expansion time consumed by GenExpan is independent of entity vocabulary and corpus size, and GenExpan achieves an average 600% speedup compared to strong baselines.

Language Modelling Retrieval

Investigating Graph Structure Information for Entity Alignment with Dangling Cases

no code implementations10 Apr 2023 Jin Xu, Yangning Li, Xiangjin Xie, Yinghui Li, Niu Hu, Haitao Zheng, Yong Jiang

To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model.

Contrastive Learning Entity Alignment +3

Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction

no code implementations30 Jun 2023 Yinghui Li, Shirong Ma, Shaoshen Chen, Haojing Huang, Shulin Huang, Yangning Li, Hai-Tao Zheng, Ying Shen

During the training process, ProTEC guides the model to learn text error correction by incorporating these sub-tasks into a progressive paradigm.

Multi-Task Learning

On the (In)Effectiveness of Large Language Models for Chinese Text Correction

no code implementations18 Jul 2023 Yinghui Li, Haojing Huang, Shirong Ma, Yong Jiang, Yangning Li, Feng Zhou, Hai-Tao Zheng, Qingyu Zhou

Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community.

Grammatical Error Correction

An Anchor Learning Approach for Citation Field Learning

no code implementations7 Sep 2023 Zilin Yuan, Borun Chen, Yimeng Dai, Yinghui Li, Hai-Tao Zheng, Rui Zhang

CIFAL leverages the anchor learning, which is model-agnostic for any Pre-trained Language Model, to help capture citation patterns from the data of different citation styles.

Language Modelling Sentence

Retrieval-Augmented Meta Learning for Low-Resource Text Classification

no code implementations10 Sep 2023 Rongsheng Li, Yangning Li, Yinghui Li, Chaiyut Luoyiching, Hai-Tao Zheng, Nannan Zhou, Hanjing Su

However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed.

Meta-Learning Retrieval +2

Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection

no code implementations10 Sep 2023 Chaiyut Luoyiching, Yangning Li, Yinghui Li, Rongsheng Li, Hai-Tao Zheng, Nannan Zhou, Hanjing Su

Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents.

Class Incremental Learning Incremental Learning +1

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters

no code implementations19 Nov 2023 Yinghui Li, Zishan Xu, Shaoshen Chen, Haojing Huang, Yangning Li, Yong Jiang, Zhongli Li, Qingyu Zhou, Hai-Tao Zheng, Ying Shen

To the best of our knowledge, Visual-C$^3$ is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

no code implementations18 Feb 2024 Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu

To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.

Grammatical Error Correction

When LLMs Meet Cunning Questions: A Fallacy Understanding Benchmark for Large Language Models

no code implementations16 Feb 2024 Yinghui Li, Qingyu Zhou, Yuanzhen Luo, Shirong Ma, Yangning Li, Hai-Tao Zheng, Xuming Hu, Philip S. Yu

In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning questions that are easy for humans to understand but difficult for models to grasp.

Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions

no code implementations25 Feb 2024 Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, Zhijiang Guo

To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.

Retrieval

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