Search Results for author: Qinliang Su

Found 28 papers, 12 papers with code

Learning to Answer Psychological Questionnaire for Personality Detection

no code implementations Findings (EMNLP) 2021 Feifan Yang, Tao Yang, Xiaojun Quan, Qinliang Su

We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire.

Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video

no code implementations8 May 2023 Zenan Xu, Xiaojun Meng, Yasheng Wang, Qinliang Su, Zexuan Qiu, Xin Jiang, Qun Liu

Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript.

Abstractive Text Summarization Language Modelling

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

2 code implementations3 Feb 2023 Bowen Tian, Qinliang Su, Jianxing Yu

When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution).

Semi-supervised Anomaly Detection supervised anomaly detection

Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization

1 code implementation31 Oct 2022 Zexuan Qiu, Qinliang Su, Jianxing Yu, Shijing Si

Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document distances.

Quantization Retrieval

Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information

no code implementations26 May 2022 Shijing Si, Jianzong Wang, Ruiyi Zhang, Qinliang Su, Jing Xiao

Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents.

Federated Learning text-classification +1

Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering

no code implementations13 May 2022 Zenan Xu, Wanjun Zhong, Qinliang Su, Zijing Ou, Fuwei Zhang

A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects.

Question Answering Semantic Composition +1

Learning Neural Set Functions Under the Optimal Subset Oracle

1 code implementation3 Mar 2022 Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian

Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery.

Anomaly Detection Drug Discovery +2

Unsupervised Hashing with Contrastive Information Bottleneck

1 code implementation13 May 2021 Zexuan Qiu, Qinliang Su, Zijing Ou, Jianxing Yu, Changyou Chen

Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible.

Contrastive Learning

Syntax-Enhanced Pre-trained Model

1 code implementation ACL 2021 Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Nan Duan, Daxin Jiang

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.

Entity Typing Question Answering +1

Constituency Lattice Encoding for Aspect Term Extraction

1 code implementation COLING 2020 Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su

One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms.

Aspect Term Extraction and Sentiment Classification Term Extraction

Low-Resource Generation of Multi-hop Reasoning Questions

no code implementations ACL 2020 Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.

Machine Reading Comprehension

Generative Semantic Hashing Enhanced via Boltzmann Machines

no code implementations ACL 2020 Lin Zheng, Qinliang Su, Dinghan Shen, Changyou Chen

Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint.

Information Retrieval Retrieval

Document Hashing with Mixture-Prior Generative Models

no code implementations IJCNLP 2019 Wei Dong, Qinliang Su, Dinghan Shen, Changyou Chen

Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes.

Information Retrieval Retrieval

A Deep Neural Information Fusion Architecture for Textual Network Embeddings

no code implementations IJCNLP 2019 Zenan Xu, Qinliang Su, Xiaojun Quan, Weijia Zhang

Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations.

On the Use of Word Embeddings Alone to Represent Natural Language Sequences

no code implementations ICLR 2018 Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Ricardo Henao, Lawrence Carin

In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.

Word Embeddings

Deconvolutional Latent-Variable Model for Text Sequence Matching

no code implementations21 Sep 2017 Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives.

Text Matching

A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks

no code implementations NeurIPS 2017 Qinliang Su, Xuejun Liao, Lawrence Carin

We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions.

Symmetric Variational Autoencoder and Connections to Adversarial Learning

2 code implementations6 Sep 2017 Liqun Chen, Shuyang Dai, Yunchen Pu, Chunyuan Li, Qinliang Su, Lawrence Carin

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence.

A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC

no code implementations4 Sep 2017 Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin

However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate.

Stochastic Optimization

Unsupervised Learning with Truncated Gaussian Graphical Models

no code implementations15 Nov 2016 Qinliang Su, Xuejun Liao, Chunyuan Li, Zhe Gan, Lawrence Carin

Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations.

Unsupervised Pre-training

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

no code implementations2 Jun 2016 Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning.

General Classification

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