Search Results for author:
Found 33 papers, 15 papers with code
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding
Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems.
Multimodal Intent Discovery from Livestream Videos
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.
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization
We show that both transfer learning methods combined achieve the highest ROUGE scores.
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
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.
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar.
StreamHover: Livestream Transcript Summarization and Annotation
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.
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding
Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval.
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
Event coreference resolution is an important research problem with many applications.
MadDog: A Web-based System for Acronym Identification and Disambiguation
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
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.
Learning to Fuse Sentences with Transformers for Summarization
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
We present an empirical study in favor of a cascade architecture to neural text summarization.
Scene Graph Modification Based on Natural Language Commands
In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user's command.
Understanding Points of Correspondence between Sentences for Abstractive Summarization
We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences.
Interaction Matching for Long-Tail Multi-Label Classification
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.
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks.
Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Finally, we find that the Label Attention heads learn relations between syntactic categories and show pathways to analyze errors.
Ranked #1 on
Dependency Parsing
on Penn Treebank
Analyzing Sentence Fusion in Abstractive Summarization
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.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Creative Procedural-Knowledge Extraction From Web Design Tutorials
Complex design tasks often require performing diverse actions in a specific order.
A System for Automated Image Editing from Natural Language Commands
This work presents the task of modifying images in an image editing program using natural language written commands.
A Web-based Framework for Collecting and Assessing Highlighted Sentences in a Document
Automatically highlighting a text aims at identifying key portions that are the most important to a reader.
Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task
We present {``}conversational image editing{''}, a novel real-world application domain combining dialogue, visual information, and the use of computer vision.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Neural abstractive summarization models have led to promising results in summarizing relatively short documents.
Ranked #4 on
Unsupervised Extractive Summarization
on Pubmed
Relationship Proposal Networks
We demonstrate the ability of our Rel-PN to localize relationships with only a few thousand proposals.
Proposing Plausible Answers for Open-ended Visual Question Answering
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.
Automatic Annotation of Structured Facts in Images
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
We show that learning visual facts in a structured way enables not only a uniform but also generalizable visual understanding.
