Search Results for author: Jing Qian

Found 21 papers, 5 papers with code

Fine-grained Entity Typing without Knowledge Base

1 code implementation EMNLP 2021 Jing Qian, Yibin Liu, Lemao Liu, Yangming Li, Haiyun Jiang, Haisong Zhang, Shuming Shi

Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.

Entity Typing named-entity-recognition +2

Language Model Detoxification in Dialogue with Contextualized Stance Control

no code implementations25 Jan 2023 Jing Qian, Xifeng Yan

To reduce the toxic degeneration in a pretrained Language Model (LM), previous work on Language Model detoxification has focused on reducing the toxicity of the generation itself (self-toxicity) without consideration of the context.

Language Modelling Response Generation

Explanations from Large Language Models Make Small Reasoners Better

no code implementations13 Oct 2022 Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, Wenhu Chen, Xifeng Yan

Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.

Explanation Generation Multi-Task Learning

Controllable Dialogue Simulation with In-Context Learning

1 code implementation9 Oct 2022 Zekun Li, Wenhu Chen, Shiyang Li, Hong Wang, Jing Qian, Xifeng Yan

Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed.

Data Augmentation Language Modelling +1

Soft MIMO Detection Using Marginal Posterior Probability Statistics

no code implementations17 Aug 2022 Jiankun Zhang, Hao Wang, Jing Qian, Zhenxing Gao

Soft demodulation of received symbols into bit log-likelihood ratios (LLRs) is at the very heart of multiple-input-multiple-output (MIMO) detection.

Limitations of Language Models in Arithmetic and Symbolic Induction

no code implementations9 Aug 2022 Jing Qian, Hong Wang, Zekun Li, Shiyang Li, Xifeng Yan

LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.

Controllable Natural Language Generation with Contrastive Prefixes

no code implementations Findings (ACL) 2022 Jing Qian, Li Dong, Yelong Shen, Furu Wei, Weizhu Chen

We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control.

Language Modelling Text Generation

Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network

no code implementations25 Apr 2021 Shurui Li, Jianqin Xu, Jing Qian, Weiping Zhang

Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences.

Towards Understanding Gender Bias in Relation Extraction

1 code implementation ACL 2020 Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang

We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE.

counterfactual Data Augmentation +3

A Benchmark Dataset for Learning to Intervene in Online Hate Speech

1 code implementation IJCNLP 2019 Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, William Yang Wang

In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.

Response Generation

Learning to Decipher Hate Symbols

no code implementations NAACL 2019 Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.

General Classification

A Survey on Natural Language Processing for Fake News Detection

1 code implementation LREC 2020 Ray Oshikawa, Jing Qian, William Yang Wang

We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.

Fake News Detection

Hierarchical CVAE for Fine-Grained Hate Speech Classification

no code implementations EMNLP 2018 Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories.

Binary Classification Classification +2

Clustering and Community Detection with Imbalanced Clusters

no code implementations26 Aug 2016 Cem Aksoylar, Jing Qian, Venkatesh Saligrama

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present.

Clustering Community Detection +1

Efficient Minimax Signal Detection on Graphs

no code implementations NeurIPS 2014 Jing Qian, Venkatesh Saligrama

Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph.

Community Detection

A Rank-SVM Approach to Anomaly Detection

no code implementations2 May 2014 Jing Qian, Jonathan Root, Venkatesh Saligrama, Yu-Ting Chen

The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density.

Anomaly Detection Test

Spectral Clustering with Imbalanced Data

no code implementations9 Sep 2013 Jing Qian, Venkatesh Saligrama

Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present.

Clustering graph partitioning

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