no code implementations • WNUT (ACL) 2021 • Yanfei Lei, Chunming Hu, Guanghui Ma, Richong Zhang
Extracting keyphrases that summarize the main points of a document is a fundamental task in natural language processing.
no code implementations • ACL 2022 • Zhuoran Li, Chunming Hu, Xiaohui Guo, Junfan Chen, Wenyi Qin, Richong Zhang
In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain.
no code implementations • Findings (ACL) 2022 • Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu
As like previous work, we rely on negative entities to encourage our model to discriminate the golden entities during training.
no code implementations • EMNLP 2020 • Runzhi Tian, Yongyi Mao, Richong Zhang
The introduction of VAE provides an efficient framework for the learning of generative models, including generative topic models.
no code implementations • COLING 2022 • Ling Ge, Chunming Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, Jihong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
Enhancing the interpretability of text classification models can help increase the reliability of these models in real-world applications.
no code implementations • 17 Apr 2024 • Zhangchi Feng, Richong Zhang, Zhijie Nie
The Composed Image Retrieval (CIR) task aims to retrieve target images using a composed query consisting of a reference image and a modified text.
2 code implementations • 20 Mar 2024 • Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Yongqiang Ma
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
no code implementations • 28 Feb 2024 • Mingxin Li, Richong Zhang, Zhijie Nie
To address these questions, we start from the perspective of gradients and discover that four effective contrastive losses can be integrated into a unified paradigm, which depends on three components: the Gradient Dissipation, the Weight, and the Ratio.
1 code implementation • 28 Oct 2023 • Chonggang Lu, Richong Zhang, Kai Sun, Jaein Kim, Cunwang Zhang, Yongyi Mao
Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities.
1 code implementation • 12 Sep 2023 • Mingxin Li, Richong Zhang, Zhijie Nie, Yongyi Mao
An intriguing phenomenon in CSE is the significant performance gap between supervised and unsupervised methods, with their only difference lying in the training data.
1 code implementation • 9 Sep 2023 • Zhijie Nie, Richong Zhang, Zhongyuan Wang, Xudong Liu
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical applications.
1 code implementation • 27 Jun 2023 • Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, Rui Zhao
Referential dialogue is a superset of various vision-language (VL) tasks.
Ranked #10 on Visual Question Answering on ViP-Bench
1 code implementation • 16 May 2023 • Junfan Chen, Richong Zhang, Zheyan Luo, Chunming Hu, Yongyi Mao
Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training.
1 code implementation • 16 May 2023 • Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes.
no code implementations • 28 Feb 2023 • Kai Sun, Richong Zhang, Samuel Mensah, Nikolaos Aletras, Yongyi Mao, Xudong Liu
Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance.
no code implementations • 11 Feb 2022 • Jirui Qi, Richong Zhang, Chune Li, Yongyi Mao
Few-shot relation classification (RC) is one of the critical problems in machine learning.
cross-domain few-shot learning Few-Shot Relation Classification
2 code implementations • 21 Jan 2022 • Qianben Chen, Richong Zhang, Yaowei Zheng, Yongyi Mao
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings.
Ranked #1 on Subjectivity Analysis on SUBJ
no code implementations • 28 May 2021 • Xiaohui Guo, Richong Zhang, Yaowei Zheng, Yongyi Mao
The ALPS regularization objective is formulated as a min-max problem, in which the outer problem is minimizing an upper-bound of the VRM loss, and the inner problem is L$_1$-ball constrained adversarial labelling on perturbed sample.
1 code implementation • CVPR 2021 • Yaowei Zheng, Richong Zhang, Yongyi Mao
This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better.
Ranked #8 on Image Classification on SVHN
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu
Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue.
1 code implementation • EMNLP 2020 • Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu
In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.
Dialogue State Tracking Multi-domain Dialogue State Tracking
no code implementations • 2 Sep 2020 • Ziqiao Wang, Yongyi Mao, Hongyu Guo, Richong Zhang
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models.
no code implementations • EMNLP 2020 • Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task.
Ranked #8 on Relation Extraction on WebNLG
1 code implementation • 12 Jan 2020 • Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, Richong Zhang
We show that IB learning is, in fact, equivalent to a special class of the quantization problem.
no code implementations • IJCNLP 2019 • Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence.
1 code implementation • IJCNLP 2019 • Junfan Chen, Richong Zhang, Yongyi Mao, Hongyu Guo, Jie Xu
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts.
no code implementations • ICLR 2020 • Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang
We prove that the family of Untied MixUp schemes is equivalent to the entire class of DAT schemes.
2 code implementations • 22 May 2019 • Hongyu Guo, Yongyi Mao, Richong Zhang
Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification.
no code implementations • 3 Mar 2019 • Bokang Zhu, Richong Zhang, Dingkun Long, Yongyi Mao
Gated models resolve this conflict by adaptively adjusting their state-update equations, whereas Vanilla RNN resolves this conflict by assigning different dimensions different tasks.
no code implementations • EMNLP 2018 • Richong Zhang, Zhiyuan Hu, Hongyu Guo, Yongyi Mao
We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation.
2 code implementations • 7 Sep 2018 • Hongyu Guo, Yongyi Mao, Richong Zhang
To address this issue, we propose a novel adaptive version of MixUp, where the mixing policies are automatically learned from the data using an additional network and objective function designed to avoid manifold intrusion.
no code implementations • COLING 2018 • Yue Wang, Richong Zhang, Cheng Xu, Yongyi Mao
In this paper, we study the problem of question answering over knowledge base.
no code implementations • 26 Jul 2018 • Hongyu Guo, Yongyi Mao, Ali Al-Bashabsheh, Richong Zhang
Based on the notion of information bottleneck (IB), we formulate a quantization problem called "IB quantization".
no code implementations • 20 Nov 2016 • Dingkun Long, Richong Zhang, Yongyi Mao
For this purpose, we design a simple and controllable task, called ``memorization problem'', where the networks are trained to memorize certain targeted information.
no code implementations • 28 Apr 2016 • Jianfeng Wen, Jian-Xin Li, Yongyi Mao, Shini Chen, Richong Zhang
The models developed to date for knowledge base embedding are all based on the assumption that the relations contained in knowledge bases are binary.