Search Results for author: Fei Liu

Found 71 papers, 28 papers with code

Modeling Endorsement for Multi-Document Abstractive Summarization

no code implementations15 Oct 2021 Logan Lebanoff, Bingqing Wang, Zhe Feng, Fei Liu

In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization.

Abstractive Text Summarization Document Summarization +1

StreamHover: Livestream Transcript Summarization and Annotation

no code implementations11 Sep 2021 Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.

Extractive Summarization

OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation

no code implementations1 Jul 2021 Jing Liu, Xinxin Zhu, Fei Liu, Longteng Guo, Zijia Zhao, Mingzhen Sun, Weining Wang, Hanqing Lu, Shiyu Zhou, Jiajun Zhang, Jinqiao Wang

In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.

Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling

1 code implementation9 May 2021 Sajiv Shah, Ayaan Haque, Fei Liu

Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing.

A Sliding-Window Approach to Automatic Creation of Meeting Minutes

1 code implementation NAACL 2021 Jia Jin Koay, Alexander Roustai, Xiaojin Dai, Fei Liu

Meeting minutes record any subject matters discussed, decisions reached and actions taken at meetings.

Lidar Point Cloud Guided Monocular 3D Object Detection

no code implementations19 Apr 2021 Liang Peng, Fei Liu, Zhengxu Yu, Senbo Yan, Dan Deng, Zheng Yang, Haifeng Liu, Deng Cai

In this paper, we propose LPCG (LiDAR point cloud guided monocular 3D object detection), which is a general framework for guiding the training of monocular 3D detectors with LiDAR point clouds.

Monocular 3D Object Detection

OCM3D: Object-Centric Monocular 3D Object Detection

no code implementations13 Apr 2021 Liang Peng, Fei Liu, Senbo Yan, Xiaofei He, Deng Cai

Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection.

Monocular 3D Object Detection

A New Approach to Overgenerating and Scoring Abstractive Summaries

1 code implementation NAACL 2021 Kaiqiang Song, Bingqing Wang, Zhe Feng, Fei Liu

We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs.

CATE: Computation-aware Neural Architecture Encoding with Transformers

1 code implementation14 Feb 2021 Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang

Our experiments show that CATE is beneficial to the downstream search, especially in the large search space.

Neural Architecture Search Representation Learning +1

Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulations

no code implementations2 Feb 2021 Jingbin Huang, Fei Liu, Florian Richter, Michael C. Yip

The fully differentiable fluid dynamics is integrated with a novel suction model for effective model predictive control of the tool.

Robotics

On a tilted Liouville-master equation of open quantum systems

no code implementations28 Jan 2021 Fei Liu

We demonstrate that it is the unraveling of the tilted quantum master equation.

Statistical Mechanics Probability

HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering

no code implementations ICCV 2021 Fei Liu, Jing Liu, Weining Wang, Hanqing Lu

Specifically, we present a novel graph memory mechanism to perform relational reasoning, and further develop two types of graph memory: a) visual graph memory that leverages visual information of video for relational reasoning; b) semantic graph memory that is specifically designed to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform relational reasoning in the semantic space.

Question Answering Relational Reasoning +1

Automatic Summarization of Open-Domain Podcast Episodes

no code implementations9 Nov 2020 Kaiqiang Song, Chen Li, Xiaoyang Wang, Dong Yu, Fei Liu

Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.

Abstractive Text Summarization

Learning to Fuse Sentences with Transformers for Summarization

1 code implementation EMNLP 2020 Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang, Fei Liu

The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.

Sentence Fusion

Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles

no code implementations16 Jul 2020 Teng Liu, Bin Tian, Yunfeng Ai, Long Chen, Fei Liu, Dongpu Cao

As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control.

Autonomous Vehicles Decision Making +1

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

no code implementations20 May 2020 Ying Li, Lingfei Ma, Zilong Zhong, Fei Liu, Dongpu Cao, Jonathan Li, Michael A. Chapman

In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.

3D Semantic Segmentation Autonomous Driving +2

AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images

1 code implementation19 May 2020 Jiaojiao Li, Chaoxiong Wu, Rui Song, Yunsong Li, Fei Liu

Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).

Development of a Flexible Coupling Framework for Coastal Inundation Studies

3 code implementations27 Mar 2020 Saeed Moghimi, Andre van der Westhuysen, Ali Abdolali, Edward Myers, Sergey Vinogradov, Zaizhong Ma, Fei Liu, Avichal Mehra, Nicole Kurkowski

To enable flexible model coupling in coastal inundation studies, a coupling framework based on ESMF/NUOPC technology under a common modeling framework called the NOAA Environmental Modeling System (NEMS) was developed.

Atmospheric and Oceanic Physics

Self-Attention Network for Skeleton-based Human Action Recognition

no code implementations18 Dec 2019 Sangwoo Cho, Muhammad Hasan Maqbool, Fei Liu, Hassan Foroosh

In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3.

Action Recognition Skeleton Based Action Recognition

Controlling the Amount of Verbatim Copying in Abstractive Summarization

1 code implementation23 Nov 2019 Kaiqiang Song, Bingqing Wang, Zhe Feng, Liu Ren, Fei Liu

In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.

Abstractive Text Summarization Language Modelling

Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

no code implementations WS 2019 Sangwoo Cho, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.

Document Summarization Extractive Summarization +2

Analyzing Sentence Fusion in Abstractive Summarization

no code implementations WS 2019 Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu

While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.

Abstractive Text Summarization Sentence Fusion

Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

1 code implementation ACL 2019 Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu

The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.

Document Summarization Multi-Document Summarization +1

End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN

no code implementations ECCV 2018 Yunlong Wang, Fei Liu, Zilei Wang, Guangqi Hou, Zhenan Sun, Tieniu Tan

Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications.

Depth Estimation

Evaluating the Utility of Hand-crafted Features in Sequence Labelling

1 code implementation EMNLP 2018 Minghao Wu, Fei Liu, Trevor Cohn

Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora.

Named Entity Recognition NER

A Novel ILP Framework for Summarizing Content with High Lexical Variety

no code implementations25 Jul 2018 Wencan Luo, Fei Liu, Zitao Liu, Diane Litman

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events.

Abstractive Text Summarization

Structure-Infused Copy Mechanisms for Abstractive Summarization

1 code implementation COLING 2018 Kaiqiang Song, Lin Zhao, Fei Liu

In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence.

Abstractive Text Summarization

Automatic Summarization of Student Course Feedback

no code implementations NAACL 2016 Wencan Luo, Fei Liu, Zitao Liu, Diane Litman

Student course feedback is generated daily in both classrooms and online course discussion forums.

Toward Abstractive Summarization Using Semantic Representations

1 code implementation HLT 2015 Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, Noah A. Smith

We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR).

Abstractive Text Summarization

An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses

no code implementations COLING 2016 Wencan Luo, Fei Liu, Diane Litman

Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner.

Text Summarization

Reinforced Extractive Summarization with Question-Focused Rewards

no code implementations ACL 2018 Kristjan Arumae, Fei Liu

We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries.

Extractive Summarization

Modeling Language Vagueness in Privacy Policies using Deep Neural Networks

no code implementations25 May 2018 Fei Liu, Nicole Lee Fella, Kexin Liao

Website privacy policies are too long to read and difficult to understand.

Narrative Modeling with Memory Chains and Semantic Supervision

1 code implementation ACL 2018 Fei Liu, Trevor Cohn, Timothy Baldwin

Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task.

Cloze Test

Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis

1 code implementation NAACL 2018 Fei Liu, Trevor Cohn, Timothy Baldwin

While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w. r. t.

Aspect-Based Sentiment Analysis Reading Comprehension

Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields

1 code implementation IJCNLP 2017 Fei Liu, Timothy Baldwin, Trevor Cohn

Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features.

A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts

no code implementations WS 2016 Fei Liu, Julien Perez, Scott Nowson

Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.

Feature Engineering Part-Of-Speech Tagging +2

A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

no code implementations EACL 2017 Fei Liu, Julien Perez, Scott Nowson

Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.

Feature Engineering Personality Trait Recognition

Gated End-to-End Memory Networks

1 code implementation EACL 2017 Julien Perez, Fei Liu

Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge.

Question Answering Reading Comprehension

Dialog state tracking, a machine reading approach using Memory Network

no code implementations EACL 2017 Julien Perez, Fei Liu

In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules.

Natural Language Understanding Question Answering +2

From Incremental Meaning to Semantic Unit (phrase by phrase)

1 code implementation17 Apr 2016 Andreas Scherbakov, Ekaterina Vylomova, Fei Liu, Timothy Baldwin

This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10.

Word Embeddings

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