Tagyoung Chung

Tagyoung Chung
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Main area of research
Natural language & AI

Tagyoung Chung is a Natural Language Processing Research Engineer His research interest is in the area of Natural Language & AI. Before joining Nuance, he was a graduate student at University of Rochester, where he received his Ph.D. in computer science. Tagyoung is interested in statistical approaches to natural language processing. Most of his research conducted during his Ph.D. program had been on machine translation. His publications generally fall into one of three broad categories: improving SCFG-based machine translation, using latent information to improve NLP tasks, and improving parameter optimization. He currently works on improving natural language understanding systems.

Selected articles

Issues concerning decoding with synchronous context-free grammar

We discuss some of the practical issues that arise from decoding with general synchronous context-free grammars. We examine problems caused by unary rules and we

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Sampling tree fragments from forests

We study the problem of sampling trees from forests, in the setting where probabilities for each tree may be a function of arbitrarily large tree

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Direct error rate minimization for statistical machine translation

Minimum error rate training is often the preferred method for optimizing parameters of statistical machine translation systems. MERT minimizes error rate by using a surrogate

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Tuning as linear regression

We propose a tuning method for statistical machine translation, based on the pairwise ranking approach. Hopkins and May (2011) presented a method that uses a

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SCFG latent annotation for machine translation

We discuss learning latent annotations for synchronous context-free grammars (SCFG) for the purpose of improving machine translation. We show that learning annotations for nonterminals results

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