Data-Driven Estimation of Backward Reachable and Invariant Sets for Unmodeled Systems via Active Learning

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

Download Full-text

Data-driven experimental design and model development using Gaussian process with active learning

Cognitive Psychology ◽

10.1016/j.cogpsych.2020.101360 ◽

2021 ◽

Vol 125 ◽

pp. 101360

Author(s):

Jorge Chang ◽

Jiseob Kim ◽

Byoung-Tak Zhang ◽

Mark A. Pitt ◽

Jay I. Myung

Keyword(s):

Experimental Design ◽

Active Learning ◽

Gaussian Process ◽

Model Development ◽

Data Driven

Download Full-text

Data-driven Verification using Efficient Active Learning

10.2514/6.2022-2086 ◽

2022 ◽

Author(s):

Venkata Vaishnav Tadiparthi ◽

Raktim Bhattacharya

Keyword(s):

Active Learning ◽

Data Driven

Download Full-text

Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3480937 ◽

2022 ◽

Vol 21 (2) ◽

pp. 1-19

Author(s):

Guirong Bai ◽

Shizhu He ◽

Kang Liu ◽

Jun Zhao

Keyword(s):

Active Learning ◽

Language Model ◽

Experimental Results ◽

Language Models ◽

Data Driven ◽

Learning Approach ◽

Sentence Matching ◽

Learning Language

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

Download Full-text