Chieh-Yang Huang 黃介揚

I am currently a fourth-year (or perhaps fifth-year) Ph.D. candidate in College of Information Sciences and Technology (IST), Penn State University. I am a research assistant in Dr. Kenneth Huang's amazing CrowdAI Lab, working on CrowdSourcing, Deep Learning, and Natural Language Processing projects. My work mainly focuses on building tools to help human's day-to-day life, especially writing and language-related works, using my NLP and HCI knowledges!

I develop various tools to support (i) creative writing, (ii) scientific writing, (iii) data annotation, and (iv) language learning.

CV / Resume
You can also find me here

PhD in Informatics
2019 - present.
PhD in CS
2017 - 2018. (Transferred)
BS in EE
2010 - 2014



Our paper, What Types of Questions Require Conversation to Answer? A Case Study of AskReddit Questions, is accepted by CHI LBW 2023.
The preprint version of Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization is available now!
Attended AAAI 2023 and introduced our work on story plot prediction in the Creative AI workshop. Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction
I will join Microsoft Research as a research intern in 2022 summer! Looking forward to working on something cool!
Our paper, Guided K-best Selection for Semantic Parsing Annotation, is accepted by ACL 2022 as a demo paper.
Our paper, Distilling Salient Reviews with Zero Labels, is accepted by FEVER Workshop (co-located with ACL 2022) as a paper.
Our paper, Extracting Salient Facts from Company Reviews with Scarce Labels, is accepted by AIBSD 2022 (co-located with AAAI 2022) as a paper.
Just passed the Ph.D. Thesis Proposal ("Toward Intelligent Writing Support Beyond Completing Sentences")! I am now a Ph.D. candidate!
Just started my internship at Semantic Machines, Microsoft Research as a research intern in 2021 summer!
Our paper, Semantic Frame Forecasting, is accepted by NAACL 2021 as a full paper! You can find the code and dataset HERE on Github.