56 citations · 199 across the 13 of their papers we have counts for
13 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…
The price of debiasing automatic metrics in natural language evaluation
Arun Tejasvi Chaganty, Stephen Mussman, Percy Liang
For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias agains…
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions
Arun Tejasvi Chaganty, Percy Liang
How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "$…
Estimating Mixture Models via Mixtures of Polynomials
Sida I. Wang, Arun Tejasvi Chaganty, Percy Liang
Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the…
On-the-Job Learning with Bayesian Decision Theory
Keenon Werling, Arun Chaganty, Percy Liang +1
Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an "on-the-job" setting, where as inputs arrive, we use real-time crowdsourcing to re…
Tensor Factorization via Matrix Factorization
Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang
Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for t…
Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings
Volodymyr Kuleshov, Arun Tesjavi Chaganty, Percy Liang
Simultaneous matrix diagonalization is used as a subroutine in many machine learning problems, including blind source separation and paramater estimation in latent variable models.…
Spectral Experts for Estimating Mixtures of Linear Regressions
Arun Tejasvi Chaganty, Percy Liang
Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima. In this paper, we develop a new computationally…