Raman Arora


2019

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Deep Generalized Canonical Correlation Analysis
Adrian Benton | Huda Khayrallah | Biman Gujral | Dee Ann Reisinger | Sheng Zhang | Raman Arora
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

We present Deep Generalized Canonical Correlation Analysis (DGCCA) – a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic & articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users.

2016

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Embedding Lexical Features via Low-Rank Tensors
Mo Yu | Mark Dredze | Raman Arora | Matthew R. Gormley
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Learning Multiview Embeddings of Twitter Users
Adrian Benton | Raman Arora | Mark Dredze
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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Multiview LSA: Representation Learning via Generalized CCA
Pushpendre Rastogi | Benjamin Van Durme | Raman Arora
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies