3 citations · 3 across the 5 of their papers we have counts for
5 papers
Mixture of Soft Prompts for Controllable Data Generation
Derek Chen, Celine Lee, Yunan Lu +2
Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns. However, structured prediction tasks confine the output form…
Data Augmentation for Intent Classification
Derek Chen, Claire Yin
Training accurate intent classifiers requires labeled data, which can be costly to obtain. Data augmentation methods may ameliorate this issue, but the quality of the generated dat…
DG2: Data Augmentation Through Document Grounded Dialogue Generation
Qingyang Wu, Song Feng, Derek Chen +3
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation. Especially in document-ground…
Clean or Annotate: How to Spend a Limited Data Collection Budget
Derek Chen, Zhou Yu, Samuel R. Bowman
Crowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling. There are…
GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation
Derek Chen, Zhou Yu
Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model…