2 citations · 2 across the 1 of their papers we have counts for
5 papers
Rethinking Why Intermediate-Task Fine-Tuning Works
Ting-Yun Chang, Chi-Jen Lu
Supplementary Training on Intermediate Labeled-data Tasks (STILTs) is a widely applied technique, which first fine-tunes the pretrained language models on an intermediate task befo…
Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense
Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan +3
Pretrained language models have demonstrated outstanding performance in many NLP tasks recently. However, their social intelligence, which requires commonsense reasoning about the…
Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks
Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan +3
Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory. To enable this, machines need to have a more genera…
TinyGAN: Distilling BigGAN for Conditional Image Generation
Ting-Yun Chang, Chi-Jen Lu
Generative Adversarial Networks (GANs) have become a powerful approach for generative image modeling. However, GANs are notorious for their training instability, especially on larg…
xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks
Ting-Yun Chang, Ta-Chung Chi, Shang-Chi Tsai +1
Despite the success achieved on various natural language processing tasks, word embeddings are difficult to interpret due to the dense vector representations. This paper focuses on…