17 citations · 42 across the 4 of their papers we have counts for
8 papers
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang +3
Users in consumption domains, like music, are often able to more efficiently provide preferences over a set of items (e.g. a playlist or radio) than over single items (e.g. songs).…
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
Megan Leszczynski, Shu Zhang, Ravi Ganti +4
Recommender systems are ubiquitous yet often difficult for users to control, and adjust if recommendation quality is poor. This has motivated conversational recommender systems (CR…
RARR: Researching and Revising What Language Models Say, Using Language Models
Luyu Gao, Zhuyun Dai, Panupong Pasupat +8
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading conte…
Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao +4
Many important questions (e.g. "How to eat healthier?") require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems…
Conformal retrofitting via Riemannian manifolds: distilling task-specific graphs into pretrained embeddings
Justin Dieter, Arun Tejasvi Chaganty
Pretrained (language) embeddings are versatile, task-agnostic feature representations of entities, like words, that are central to many machine learning applications. These represe…
Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts
Matthew Lamm, Arun Tejasvi Chaganty, Christopher D. Manning +2
To understand a sentence like "whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do" it is important not only to identify individual f…