565 citations · 758 across the 16 of their papers we have counts for
8 papers · 2 filters
Analyzing the Limits of Self-Supervision in Handling Bias in Language
Lisa Bauer, Karthik Gopalakrishnan, Spandana Gella +3
Prompting inputs with natural language task descriptions has emerged as a popular mechanism to elicit reasonably accurate outputs from large-scale generative language models with l…
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia +5
Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting u…
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Seokhwan Kim, Yang Liu, Di Jin +4
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets. However, written dialogues are not sufficient to fully capture the natu…
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia +5
Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and Commonsen…
Generative Conversational Networks
Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar +3
Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to…
Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense
Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan +3
Pretrained language models have demonstrated outstanding performance in many NLP tasks recently. However, their social intelligence, which requires commonsense reasoning about the…