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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ... Zhu, Yi; Shareghi, Ehsan; Li, Yingzhen. - : arXiv, 2021 BASE Show details 2 A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ... Zhao, Mengjie; Zhu, Yi; Shareghi, Ehsan. - : arXiv, 2020 BASE Show details 3 Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees ... Shareghi, Ehsan; Petri, Matthias; Haffari, Gholamreza. - : arXiv, 2016 BASE Show details 4 Structured Prediction of Sequences and Trees using Infinite Contexts ... Shareghi, Ehsan; Haffari, Gholamreza; Cohn, Trevor; Nicholson, Ann. - : arXiv, 2015 Abstract: Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions. We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions. This builds on the success of Markov models but without imposing a fixed bound in order to better represent global phenomena. To facilitate learning of this large and unbounded model, we use a hierarchical Pitman-Yor process prior which provides a recursive form of smoothing. We propose prediction algorithms based on A* and Markov Chain Monte Carlo sampling. Empirical results demonstrate the potential of our model compared to baseline finite-context Markov models on part-of-speech tagging and syntactic parsing. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/1503.02417 https://dx.doi.org/10.48550/arxiv.1503.02417 BASE Hide details

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