K
Kai-Wei Chang
Researcher at University of California, Los Angeles
Publications - 262
Citations - 23031
Kai-Wei Chang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Word embedding. The author has an hindex of 42, co-authored 183 publications receiving 17271 citations. Previous affiliations of Kai-Wei Chang include Boston University & Amazon.com.
Papers
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Proceedings ArticleDOI
An Analysis of The Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation
TL;DR: A systematic analysis of the impact of decoding algorithms on LM fairness, and recommendations on standardized reporting of decoding details in fairness evaluations and optimization of decode algorithms for fairness alongside quality and diversity are provided.
Proceedings Article
Resource Constrained Structured Prediction
TL;DR: In this paper, a novel approach based on selectively acquiring computationally costly features during test-time in order to reduce the computational cost of pre- diction with minimal performance degradation is proposed.
Journal ArticleDOI
Watermarking Pre-trained Language Models with Backdooring
TL;DR: It is shown that PLMs can be watermarked with a multi-task learning framework by embedding backdoors triggered by specific inputs defluenced by the owners, and those watermarks are hard to remove even though the watermarked PLMs are watermarked on multiple downstream tasks.
Journal ArticleDOI
DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation
TL;DR: The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veg-anism/vegetarianism, posing multiple unique challenges: limited data and label imbalance, reliance on external knowledge, multimodal reasoning, layout dependency, and noise from OCR.
Posted Content
Select, Extract and Generate: Neural Keyphrase Generation with Syntactic Guidance.
TL;DR: SEG-Net is proposed, a neural keyphrase generation model that is composed of two major components, a selector that selects the salient sentences in a document, and an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.