Search Results for author: Zhiting Hu

Found 55 papers, 24 papers with code

Knowledge-Enriched Natural Language Generation

1 code implementation EMNLP (ACL) 2021 Wenhao Yu, Meng Jiang, Zhiting Hu, Qingyun Wang, Heng Ji, Nazneen Rajani

Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge.

Text Generation

elBERto: Self-supervised Commonsense Learning for Question Answering

no code implementations17 Mar 2022 Xunlin Zhan, Yuan Li, Xiao Dong, Xiaodan Liang, Zhiting Hu, Lawrence Carin

Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context.

Question Answering Representation Learning

A Causal Lens for Controllable Text Generation

no code implementations NeurIPS 2021 Zhiting Hu, Li Erran Li

Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i. e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i. e., text attribute transfer).

Causal Inference Text Attribute Transfer +1

Text Generation with Efficient (Soft) $Q$-Learning

no code implementations29 Sep 2021 Han Guo, Bowen Tan, Zhengzhong Liu, Eric Xing, Zhiting Hu

We apply the approach to a wide range of text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.

Q-Learning Text Generation

Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation

1 code implementation EMNLP 2021 Mingkai Deng, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu

Based on the nature of information change from input to output, we classify NLG tasks into compression (e. g., summarization), transduction (e. g., text rewriting), and creation (e. g., dialog).

Style Transfer Text Generation +1

Panoramic Learning with A Standardized Machine Learning Formalism

no code implementations17 Aug 2021 Zhiting Hu, Eric P. Xing

Machine Learning (ML) is about computational methods that enable machines to learn concepts from experiences.

Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation

1 code implementation29 Jun 2021 Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu

Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.

Machine Translation Style Transfer +3

Text Generation with Efficient (Soft) Q-Learning

1 code implementation14 Jun 2021 Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu

We apply the approach to a wide range of text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.

Q-Learning Text Generation

A Data-Centric Framework for Composable NLP Workflows

1 code implementation EMNLP 2020 Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Haoying Zhang, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu

Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.

Deep Learning for Text Style Transfer: A Survey

2 code implementations1 Nov 2020 Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.

Style Transfer Text Attribute Transfer +1

Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach

1 code implementation EMNLP 2020 Bowen Tan, Lianhui Qin, Eric P. Xing, Zhiting Hu

Given a document and a target aspect (e. g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect.

Abstractive Text Summarization

A Survey of Knowledge-Enhanced Text Generation

3 code implementations9 Oct 2020 Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang

To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.

Text Generation

Progressive Generation of Long Text with Pretrained Language Models

1 code implementation NAACL 2021 Bowen Tan, Zichao Yang, Maruan AI-Shedivat, Eric P. Xing, Zhiting Hu

However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e. g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus.

Pretrained Language Models

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

1 code implementation NeurIPS 2020 Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation.

Image Generation Style Transfer +1

Learning Data Manipulation for Augmentation and Weighting

1 code implementation NeurIPS 2019 Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing

In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm.

Data Augmentation reinforcement-learning +1

Graph Transformer

no code implementations ICLR 2019 Yuan Li, Xiaodan Liang, Zhiting Hu, Yinbo Chen, Eric P. Xing

Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.

Few-Shot Learning General Classification +3

Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

no code implementations25 Mar 2019 Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing

Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions.

Graph Learning Knowledge Graphs +2

Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction

no code implementations20 Mar 2019 Lu-chen Liu, Haoran Li, Zhiting Hu, Haoran Shi, Zichang Wang, Jian Tang, Ming Zhang

Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.

Data-to-Text Generation with Style Imitation

1 code implementation Findings of the Association for Computational Linguistics 2020 Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.

Data-to-Text Generation Style Transfer

Text Infilling

1 code implementation1 Jan 2019 Wanrong Zhu, Zhiting Hu, Eric Xing

Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting.

Text Infilling

Symbolic Graph Reasoning Meets Convolutions

1 code implementation NeurIPS 2018 Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing

To cooperate with local convolutions, each SGR is constituted by three modules: a) a primal local-to-semantic voting module where the features of all symbolic nodes are generated by voting from local representations; b) a graph reasoning module propagates information over knowledge graph to achieve global semantic coherency; c) a dual semantic-to-local mapping module learns new associations of the evolved symbolic nodes with local representations, and accordingly enhances local features.

Image Classification Semantic Segmentation

Connecting the Dots Between MLE and RL for Sequence Prediction

no code implementations24 Nov 2018 Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric Xing

Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.

Imitation Learning Machine Translation +2

Structured Content Preservation for Unsupervised Text Style Transfer

2 code implementations15 Oct 2018 Youzhi Tian, Zhiting Hu, Zhou Yu

Text style transfer aims to modify the style of a sentence while keeping its content unchanged.

Language Modelling Style Transfer +2

AutoLoss: Learning Discrete Schedules for Alternate Optimization

1 code implementation4 Oct 2018 Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.

Image Generation Machine Translation +2

Differentiable Expected BLEU for Text Generation

no code implementations27 Sep 2018 Wentao Wang, Zhiting Hu, Zichao Yang, Haoran Shi, Eric P. Xing

Neural text generation models such as recurrent networks are typically trained by maximizing data log-likelihood based on cross entropy.

Image Captioning Machine Translation +2

AutoLoss: Learning Discrete Schedule for Alternate Optimization

no code implementations ICLR 2019 Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.

Image Generation Machine Translation +1

Unsupervised Text Style Transfer using Language Models as Discriminators

1 code implementation NeurIPS 2018 Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick

Binary classifiers are often employed as discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain.

Decipherment Language Modelling +4

Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

no code implementations NeurIPS 2018 Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing

Experiments show that our approach achieves the state-of-the-art results on two medical report datasets, generating well-balanced structured sentences with robust coverage of heterogeneous medical report contents.

Decision Making

Towards Automated ICD Coding Using Deep Learning

no code implementations11 Nov 2017 Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, Eric P. Xing

Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis.

General Classification

Efficient Correlated Topic Modeling with Topic Embedding

no code implementations1 Jul 2017 Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling.

Document Classification General Classification +1

Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

no code implementations11 Jun 2017 Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing

We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.

Image Classification

On Unifying Deep Generative Models

no code implementations ICLR 2018 Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing

Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively.

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

no code implementations ACL 2017 Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, Eric P. Xing

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition.

Classification General Classification +1

Recurrent Topic-Transition GAN for Visual Paragraph Generation

no code implementations ICCV 2017 Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing

The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators.

Image Paragraph Captioning

Nonparametric Variational Auto-encoders for Hierarchical Representation Learning

no code implementations ICCV 2017 Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric Xing

In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.

Representation Learning Variational Inference

Toward Controlled Generation of Text

3 code implementations ICML 2017 Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain.

Improved Variational Autoencoders for Text Modeling using Dilated Convolutions

3 code implementations ICML 2017 Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick

Recent work on generative modeling of text has found that variational auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM language models (Bowman et al., 2015).

Text Generation

Stochastic Variational Deep Kernel Learning

no code implementations NeurIPS 2016 Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

We propose a novel deep kernel learning model and stochastic variational inference procedure which generalizes deep kernel learning approaches to enable classification, multi-task learning, additive covariance structures, and stochastic gradient training.

Gaussian Processes General Classification +2

Dropout with Expectation-linear Regularization

no code implementations26 Sep 2016 Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yao-Liang Yu, Yuntian Deng, Eduard Hovy

Algorithmically, we show that our proposed measure of the inference gap can be used to regularize the standard dropout training objective, resulting in an \emph{explicit} control of the gap.

Image Classification

Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification

no code implementations COLING 2016 Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara

Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information.

General Classification

Neural Machine Translation with Recurrent Attention Modeling

no code implementations EACL 2017 Zichao Yang, Zhiting Hu, Yuntian Deng, Chris Dyer, Alex Smola

Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future.

Machine Translation Translation

Joint Embeddings of Hierarchical Categories and Entities

no code implementations12 May 2016 Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, Katia Sycara

Due to the lack of structured knowledge applied in learning distributed representation of categories, existing work cannot incorporate category hierarchies into entity information.~We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases.

Harnessing Deep Neural Networks with Logic Rules

2 code implementations ACL 2016 Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric Xing

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.

Named Entity Recognition Sentiment Analysis

Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

no code implementations19 Dec 2015 Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing

To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks.

Object Recognition

Deep Kernel Learning

4 code implementations6 Nov 2015 Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods.

Gaussian Processes

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