3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
Ernie Chang, Jeriah Caplinger, Alex Marin +2
We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an…