b'Zhiting Hu'

research

∙ 07/06/2023

Text Alignment Is An Efficient Unified Model for Massive NLP Tasks

Large language models (LLMs), typically designed as a function of next-w...

0 Yuheng Zha, et al. ∙

research

∙ 05/26/2023

AlignScore: Evaluating Factual Consistency with a Unified Alignment Function

Many text generation applications require the generated text to be factu...

0 Yuheng Zha, et al. ∙

research

∙ 05/24/2023

Reasoning with Language Model is Planning with World Model

Large language models (LLMs) have shown remarkable reasoning capabilitie...

0 Shibo Hao, et al. ∙

research

∙ 05/19/2023

ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings

Augmenting large language models (LLMs) with external tools has emerged ...

0 Shibo Hao, et al. ∙

research

∙ 05/18/2023

Language Models Meet World Models: Embodied Experiences Enhance Language Models

While large language models (LMs) have shown remarkable capabilities acr...

0 Jiannan Xiang, et al. ∙

research

∙ 10/09/2022

ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models

Data-to-text generation is challenging due to the great variety of the i...

0 Jiannan Xiang, et al. ∙

research

∙ 08/01/2022

Composable Text Control Operations in Latent Space with Ordinary Differential Equations

Real-world text applications often involve composing a wide range of tex...

3 Guangyi Liu, et al. ∙

research

∙ 06/28/2022

BertNet: Harvesting Knowledge Graphs from Pretrained Language Models

Symbolic knowledge graphs (KGs) have been constructed either by expensiv...

22 Shibo Hao, et al. ∙

research

∙ 05/25/2022

RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning

Prompting has shown impressive success in enabling large pretrained lang...

9 Mingkai Deng, et al. ∙

research

∙ 01/22/2022

A Causal Lens for Controllable Text Generation

Controllable text generation concerns two fundamental tasks of wide appl...

0 Zhiting Hu, et al. ∙

research

∙ 09/14/2021

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

Natural language generation (NLG) spans a broad range of tasks, each of ...

31 Mingkai Deng, et al. ∙

research

∙ 08/17/2021

Panoramic Learning with A Standardized Machine Learning Formalism

Machine Learning (ML) is about computational methods that enable machine...

7 Zhiting Hu, et al. ∙

research

∙ 06/29/2021

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

Neural text generation models are typically trained by maximizing log-li...

0 Guangyi Liu, et al. ∙

research

∙ 06/14/2021

Text Generation with Efficient (Soft) Q-Learning

Maximum likelihood estimation (MLE) is the predominant algorithm for tra...

2 Han Guo, et al. ∙

research

∙ 03/02/2021

A Data-Centric Framework for Composable NLP Workflows

Empirical natural language processing (NLP) systems in application domai...

17 Zhengzhong Liu, et al. ∙

research

∙ 10/14/2020

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

Given a document and a target aspect (e.g., a topic of interest), aspect...

0 Bowen Tan, et al. ∙

research

∙ 10/09/2020

A Survey of Knowledge-Enhanced Text Generation

The goal of text generation is to make machines express in human languag...

13 Wenhao Yu, et al. ∙

research

∙ 06/28/2020

Progressive Generation of Long Text

Large-scale language models pretrained on massive corpora of text, such ...

0 Bowen Tan, et al. ∙

research

∙ 06/12/2020

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Despite success on a wide range of problems related to vision, generativ...

15 Yue Wu, et al. ∙

research

∙ 10/28/2019

Learning Data Manipulation for Augmentation and Weighting

Manipulating data, such as weighting data examples or augmenting with ne...

30 Zhiting Hu, et al. ∙

research

∙ 05/28/2019

Target-Guided Open-Domain Conversation

Many real-world open-domain conversation applications have specific goal...

0 Jianheng Tang, et al. ∙

research

∙ 03/25/2019

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

Generating long and semantic-coherent reports to describe medical images...

8 Christy Y. Li, et al. ∙

research

∙ 03/20/2019

Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction

Clinical outcome prediction based on the Electronic Health Record (EHR) ...

36 Luchen Liu, et al. ∙

research

∙ 01/28/2019

Toward Unsupervised Text Content Manipulation

Controlled generation of text is of high practical use. Recent efforts h...

6 Wentao Wang, et al. ∙

research

∙ 01/01/2019

Text Infilling

Recent years have seen remarkable progress of text generation in differe...

0 Wanrong Zhu, et al. ∙

research

∙ 11/24/2018

Connecting the Dots Between MLE and RL for Sequence Generation

Sequence generation models such as recurrent networks can be trained wit...

0 Bowen Tan, et al. ∙

research

∙ 10/15/2018

Structured Content Preservation for Unsupervised Text Style Transfer

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

0 Youzhi Tian, et al. ∙

research

∙ 10/04/2018

AutoLoss: Learning Discrete Schedules for Alternate Optimization

Many machine learning problems involve iteratively and alternately optim...

8 Haowen Xu, et al. ∙

research

∙ 09/04/2018

Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation

We introduce Texar, an open-source toolkit aiming to support the broad s...

0 Zhiting Hu, et al. ∙

research

∙ 06/26/2018

Deep Generative Models with Learnable Knowledge Constraints

The broad set of deep generative models (DGMs) has achieved remarkable a...

4 Zhiting Hu, et al. ∙

research

∙ 05/30/2018

Unsupervised Text Style Transfer using Language Models as Discriminators

Binary classifiers are often employed as discriminators in GAN-based uns...

0 Zichao Yang, et al. ∙

research

∙ 05/21/2018

Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

Generating long and coherent reports to describe medical images poses ch...

0 Christy Y. Li, et al. ∙

research

∙ 05/09/2018

Automatic Article Commenting: the Task and Dataset

Comments of online articles provide extended views and improve user enga...

0 Lianhui Qin, et al. ∙

research

∙ 11/11/2017

Towards Automated ICD Coding Using Deep Learning

International Classification of Diseases(ICD) is an authoritative health...

0 Haoran Shi, et al. ∙

research

∙ 07/01/2017

Efficient Correlated Topic Modeling with Topic Embedding

Correlated topic modeling has been limited to small model and problem si...

0 Junxian He, et al. ∙

research

∙ 06/11/2017

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

Deep learning models can take weeks to train on a single GPU-equipped ma...

0 Hao Zhang, et al. ∙

research

∙ 06/02/2017

On Unifying Deep Generative Models

Deep generative models have achieved impressive success in recent years....

0 Zhiting Hu, et al. ∙

research

∙ 04/01/2017

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Implicit discourse relation classification is of great challenge due to ...

0 Lianhui Qin, et al. ∙

research

∙ 03/21/2017

Nonparametric Variational Auto-encoders for Hierarchical Representation Learning

The recently developed variational autoencoders (VAEs) have proved to be...

0 Prasoon Goyal, et al. ∙

research

∙ 03/21/2017

Recurrent Topic-Transition GAN for Visual Paragraph Generation

A natural image usually conveys rich semantic content and can be viewed ...

0 Xiaodan Liang, et al. ∙

research

∙ 03/02/2017

Toward Controlled Generation of Text

Generic generation and manipulation of text is challenging and has limit...

0 Zhiting Hu, et al. ∙

research

∙ 02/27/2017

Improved Variational Autoencoders for Text Modeling using Dilated Convolutions

Recent work on generative modeling of text has found that variational au...

0 Zichao Yang, et al. ∙

research

∙ 11/01/2016

Stochastic Variational Deep Kernel Learning

Deep kernel learning combines the non-parametric flexibility of kernel m...

0 Andrew Gordon Wilson, et al. ∙

research

∙ 10/27/2016

Learning Scalable Deep Kernels with Recurrent Structure

Many applications in speech, robotics, finance, and biology deal with se...

0 Maruan Al-Shedivat, et al. ∙

research

∙ 09/26/2016

Dropout with Expectation-linear Regularization

Dropout, a simple and effective way to train deep neural networks, has l...

0 Xuezhe Ma, et al. ∙

research

∙ 07/27/2016

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

Due to the lack of structured knowledge applied in learning distributed ...

0 Yuezhang Li, et al. ∙

research

∙ 07/18/2016

Neural Machine Translation with Recurrent Attention Modeling

Knowing which words have been attended to in previous time steps while g...

0 Zichao Yang, et al. ∙

research

∙ 06/29/2016

Learning Concept Taxonomies from Multi-modal Data

We study the problem of automatically building hypernym taxonomies from ...

0 Hao Zhang, et al. ∙

research

∙ 05/12/2016

Joint Embeddings of Hierarchical Categories and Entities

Due to the lack of structured knowledge applied in learning distributed ...

0 Yuezhang Li, et al. ∙

research

∙ 03/21/2016

Harnessing Deep Neural Networks with Logic Rules

Combining deep neural networks with structured logic rules is desirable ...

0 Zhiting Hu, et al. ∙

Zhiting Hu

Featured Co-authors