Search Results for author: Arun Tejasvi Chaganty

Found 8 papers, 5 papers with code

Conformal retrofitting via Riemannian manifolds: distilling task-specific graphs into pretrained embeddings

no code implementations9 Oct 2020 Justin Dieter, Arun Tejasvi Chaganty

We address these problems with two key contributions: (i) we propose a novel regularizer, a conformality regularizer, that preserves local geometry from the pretrained embeddings---enabling generalization to missing entities and (ii) a new Riemannian feedforward layer that learns to map pre-trained embeddings onto a non-Euclidean manifold that can better represent the entire graph.

Link Prediction

Mimic and Rephrase: Reflective Listening in Open-Ended Dialogue

no code implementations CONLL 2019 Justin Dieter, Tian Wang, Arun Tejasvi Chaganty, Gabor Angeli, Angel X. Chang

Reflective listening{--}demonstrating that you have heard your conversational partner{--}is key to effective communication.

Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts

2 code implementations EMNLP 2018 Matthew Lamm, Arun Tejasvi Chaganty, Christopher D. Manning, Dan Jurafsky, Percy Liang

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 facts, e. g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e. g., the disparity between them.

Textual Analogy Parsing

The price of debiasing automatic metrics in natural language evaluation

1 code implementation6 Jul 2018 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 against certain model improvements.

Question Answering

How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

1 code implementation ACL 2016 Arun Tejasvi Chaganty, Percy Liang

We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation.

Estimating Mixture Models via Mixtures of Polynomials

3 code implementations NeurIPS 2015 Sida I. Wang, Arun Tejasvi Chaganty, Percy Liang

This framework allows us to draw insights and apply tools from convex optimization, computer algebra and the theory of moments to study problems in statistical estimation.

Tensor Factorization via Matrix Factorization

1 code implementation29 Jan 2015 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.

Latent Variable Models

Spectral Experts for Estimating Mixtures of Linear Regressions

no code implementations17 Jun 2013 Arun Tejasvi Chaganty, Percy Liang

Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima.

Latent Variable Models

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