10 citations · 17 across the 8 of their papers we have counts for
10 papers
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…
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning
Ernie Chang, Hui-Syuan Yeh, Vera Demberg
Recent advancements in data-to-text generation largely take on the form of neural end-to-end systems. Efforts have been dedicated to improving text generation systems by changing t…
Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
Ernie Chang, Vera Demberg, Alex Marin
Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address t…