565 citations · 624 across the 11 of their papers we have counts for
12 papers
Revisiting Sample Size Determination in Natural Language Understanding
Ernie Chang, Muhammad Hassan Rashid, Pin-Jie Lin +4
Knowing exactly how many data points need to be labeled to achieve a certain model performance is a hugely beneficial step towards reducing the overall budgets for annotation. It p…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Ernie Chang, Xiaoyu Shen, Alex Marin +1
We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot te…
Time-Aware Ancient Chinese Text Translation and Inference
Ernie Chang, Yow-Ting Shiue, Hui-Syuan Yeh +1
In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations th…
On Training Instance Selection for Few-Shot Neural Text Generation
Ernie Chang, Xiaoyu Shen, Hui-Syuan Yeh +1
Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instance…
Neural Data-to-Text Generation with LM-based Text Augmentation
Ernie Chang, Xiaoyu Shen, Dawei Zhu +2
For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instan…