Search Results for author:
Found 26 papers, 16 papers with code
Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers
In this study, we investigate the impact of different datasets on model performance for the crowd-annotated CODA-19 research aspect classification task.
ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.
What Types of Questions Require Conversation to Answer? A Case Study of AskReddit Questions
The proliferation of automated conversational systems such as chatbots, spoken-dialogue systems, and smart speakers, has significantly impacted modern digital life.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Prior work often treated figure caption generation as a vision-to-language task.
Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction
Next, we conducted a preliminary user study using a story continuation task where AMT workers were given access to machine-generated story plots and asked to write a follow-up story.
Nationality Bias in Text Generation
Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models.
Are Shortest Rationales the Best Explanations for Human Understanding?
Existing self-explaining models typically favor extracting the shortest possible rationales - snippets of an input text "responsible for" corresponding output - to explain the model prediction, with the assumption that shorter rationales are more intuitive to humans.
SciCap: Generating Captions for Scientific Figures
Researchers use figures to communicate rich, complex information in scientific papers.
Ranked #1 on
Image Captioning
on SCICAP
Empowering Local Communities Using Artificial Intelligence
Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition.
Plot and Rework: Modeling Storylines for Visual Storytelling
Writing a coherent and engaging story is not easy.
Ranked #27 on
Visual Storytelling
on VIST
Semantic Frame Forecast
In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e. g., 10, 100, or 200).
Explaining the Road Not Taken
It is unclear if existing interpretations of deep neural network models respond effectively to the needs of users.
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent
The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently.
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health.
CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset
This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10, 966 English abstracts in the COVID-19 Open Research Dataset.
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities.
Knowledge-Enriched Visual Storytelling
This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories.
On Automating Conversations
From 2016 to 2018, we developed and deployed Chorus, a system that blends real-time human computation with artificial intelligence (AI) and has real-world, open conversations with users.
InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd
The user verbally expresses a problem to the system, in which a group of crowd workers collectively respond and program relevant multi-part IF-THEN rules to help the user.
Visual Story Post-Editing
We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task.
Dixit: Interactive Visual Storytelling via Term Manipulation
In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence.
On How Users Edit Computer-Generated Visual Stories
A significant body of research in Artificial Intelligence (AI) has focused on generating stories automatically, either based on prior story plots or input images.
Community-Empowered Mobile Smell Reporting System
Taking this into account, we developed Smell Pittsburgh, a system that enables communities to report odors and track where these odors are frequently concentrated.
Human-Computer Interaction
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs.
Real-time On-Demand Crowd-powered Entity Extraction
Output-agreement mechanisms such as ESP Game have been widely used in human computation to obtain reliable human-generated labels.
