8 citations · 32 across the 9 of their papers we have counts for
14 papers
MadDog: A Web-based System for Acronym Identification and Disambiguation
Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang +1
Acronyms and abbreviations are the short-form of longer phrases and they are ubiquitously employed in various types of writing. Despite their usefulness to save space in writing an…
Learning to Fuse Sentences with Transformers for Summarization
Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim +3
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail o…
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim +2
We present an empirical study in favor of a cascade architecture to neural text summarization. Summarization practices vary widely but few other than news summarization can provide…
Interaction Matching for Long-Tail Multi-Label Classification
Sean MacAvaney, Franck Dernoncourt, Walter Chang +2
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 u…
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Kang Min Yoo, Hanbit Lee, Franck Dernoncourt +3
Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this…
Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Khalil Mrini, Franck Dernoncourt, Quan Tran +3
Attention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used, however interpretability is di…
Analyzing Sentence Fusion in Abstractive Summarization
Logan Lebanoff, John Muchovej, Franck Dernoncourt +4
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…
Scoring Sentence Singletons and Pairs for Abstractive Summarization
Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt +4
When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one o…
Creative Procedural-Knowledge Extraction From Web Design Tutorials
Longqi Yang, Chen Fang, Hailin Jin +2
Complex design tasks often require performing diverse actions in a specific order. To (semi-)autonomously accomplish these tasks, applications need to understand and learn a wide r…
A System for Automated Image Editing from Natural Language Commands
Jacqueline Brixey, Ramesh Manuvinakurike, Nham Le +3
This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to al…
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim +4
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single,…
Proposing Plausible Answers for Open-ended Visual Question Answering
Omid Bakhshandeh, Trung Bui, Zhe Lin +1
Answering open-ended questions is an essential capability for any intelligent agent. One of the most interesting recent open-ended question answering challenges is Visual Question…
Automatic Annotation of Structured Facts in Images
Mohamed Elhoseiny, Scott Cohen, Walter Chang +2
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. Example str…
Sherlock: Scalable Fact Learning in Images
Mohamed Elhoseiny, Scott Cohen, Walter Chang +2
We study scalable and uniform understanding of facts in images. Existing visual recognition systems are typically modeled differently for each fact type such as objects, actions, a…