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Found 32 papers, 10 papers with code
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
Towards a Progression-Aware Autonomous Dialogue Agent
Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization
Generating educational questions of fairytales or storybooks is vital for improving children's literacy ability.
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on
Question Generation
on FairytaleQA
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Creating presentation slides is a critical but time-consuming task for data scientists.
DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance
Geoscientists, as well as researchers in many fields, need to read a huge amount of literature to locate, extract, and aggregate relevant results and data to enable future research or to build a scientific database, but there is no existing system to support this use case well.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.
Human-Centered AI for Data Science: A Systematic Approach
Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control.
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
D2S: Document-to-Slide Generation Via Query-Based Text Summarization
Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming.
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells.
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation
Prior works on formalizing explanations of a graph neural network (GNN) focus on a single use case - to preserve the prediction results through identifying important edges and nodes.
AutoDS: Towards Human-Centered Automation of Data Science
Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these tasks.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team.
How Much Automation Does a Data Scientist Want?
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS).
CASS: Towards Building a Social-Support Chatbot for Online Health Community
Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community.
Solving Constrained CASH Problems with ADMM
The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.
DeepBrain: Towards Personalized EEG Interaction through Attentional and Embedded LSTM Learning
The "mind-controlling" capability has always been in mankind's fantasy.
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use.
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML).
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow.
Context-Aware Conversation Thread Detection in Multi-Party Chat
In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.
How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process.
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task.
Group Chat Ecology in Enterprise Instant Messaging: How Employees Collaborate Through Multi-User Chat Channels on Slack
We cross-referenced 117 project teams and their team-based Slack channels and identified 57 teams that appeared in both datasets, then we built a regression model to reveal the relationship between these group communication styles and the project team performance.
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph.
Ranked #10 on
Relation Extraction
on SemEval-2010 Task 8
