Search Results for author: Walter Chang

Found 33 papers, 15 papers with code

Multimodal Intent Discovery from Livestream Videos

no code implementations Findings (NAACL) 2022 Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal

We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models.

Intent Discovery Video Summarization +1

Joint Summarization-Entailment Optimization for Consumer Health Question Understanding

1 code implementation NAACL (NLPMC) 2021 Khalil Mrini, Franck Dernoncourt, Walter Chang, Emilia Farcas, Ndapa Nakashole

Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems.

Data Augmentation

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

1 code implementation COLING 2022 Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Emilia Farcas, Ndapa Nakashole

The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document.

Question Answering Retrieval

StreamHover: Livestream Transcript Summarization and Annotation

1 code implementation EMNLP 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

MadDog: A Web-based System for Acronym Identification and Disambiguation

1 code implementation EACL 2021 Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, Thien Huu Nguyen

However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available.

Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding

no code implementations22 Dec 2020 Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang, Leo Anthony Celi

To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively.

document understanding

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

Interaction Matching for Long-Tail Multi-Label Classification

no code implementations18 May 2020 Sean MacAvaney, Franck Dernoncourt, Walter Chang, Nazli Goharian, Ophir Frieder

We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking.

Classification General Classification +1

Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation

1 code implementation EMNLP 2020 Kang Min Yoo, Hanbit Lee, Franck Dernoncourt, Trung Bui, Walter Chang, Sang-goo Lee

Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks.

Data Augmentation dialog state tracking +4

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

A System for Automated Image Editing from Natural Language Commands

no code implementations3 Dec 2018 Jacqueline Brixey, Ramesh Manuvinakurike, Nham Le, Tuan Lai, Walter Chang, Trung Bui

This work presents the task of modifying images in an image editing program using natural language written commands.

Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task

no code implementations WS 2018 Ramesh Manuvinakurike, Trung Bui, Walter Chang, Kallirroi Georgila

We present {``}conversational image editing{''}, a novel real-world application domain combining dialogue, visual information, and the use of computer vision.

General Classification

Relationship Proposal Networks

no code implementations CVPR 2017 Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, Ahmed Elgammal

We demonstrate the ability of our Rel-PN to localize relationships with only a few thousand proposals.

Scene Understanding

Proposing Plausible Answers for Open-ended Visual Question Answering

no code implementations20 Oct 2016 Omid Bakhshandeh, Trung Bui, Zhe Lin, Walter Chang

One of the most interesting recent open-ended question answering challenges is Visual Question Answering (VQA) which attempts to evaluate a system's visual understanding through its answers to natural language questions about images.

Graph Matching Open-Ended Question Answering +1

Automatic Annotation of Structured Facts in Images

no code implementations WS 2016 Mohamed Elhoseiny, Scott Cohen, Walter Chang, Brian Price, Ahmed Elgammal

Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions.

Sherlock: Scalable Fact Learning in Images

no code implementations16 Nov 2015 Mohamed Elhoseiny, Scott Cohen, Walter Chang, Brian Price, Ahmed Elgammal

We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding.

Multiview Learning Retrieval

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