1 code implementation • 15 Dec 2023 • Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara, Hakim Sidahmed
Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement.
no code implementations • 16 Nov 2022 • Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren
Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations.
no code implementations • 14 Jul 2022 • Eriq Augustine, Pegah Jandaghi, Alon Albalak, Connor Pryor, Charles Dickens, William Wang, Lise Getoor
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
1 code implementation • 12 May 2022 • Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
no code implementations • Findings (EMNLP) 2021 • Pei Zhou, Pegah Jandaghi, Bill Yuchen Lin, Justin Cho, Jay Pujara, Xiang Ren
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations.
no code implementations • 16 Jan 2020 • Pegah Jandaghi, Jay Pujara
Our system finds patterns in time series data and ranks these patterns based on empirical observations of human behavior using utility estimation.