Grammar Induction, Representation of Language and Language Learning
Alex Clark · Dorota Glowacka · John Shawe-Taylor · Yee Whye Teh · Chris J Watkins

Fri Dec 11th 07:30 AM -- 06:30 PM @ Hilton: Sutcliffe A
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Now is the time to revisit some of the fundamental grammar/language learning tasks such as grammar acquisition, language acquisition, language change, and the general problem of automatically inferring generic representations of language structure in a data driven manner.

Though the underlying problems have been known to be computationally intractable for the standard representations of the Chomsky hierarchy, such as regular grammars and context free grammars, progress has been made by modifying or restricting these classes to make them more observable. Generalisations of distributional learning have shown promise in unsupervised learning of linguistic structure using tree based representations, or using non-parametric approaches to inference. More radically, significant advances in this domain have been made by switching to different representations such as the work in Clark, Eyrand & Habrard (2008) that addresses the issue of language acquisition, but has the potential to cross-fertilise a wide range of problems that require data driven representations of language. Such approaches are starting to make inroads into one of the fundamental problems of cognitive science: that of learning complex representations that encode meaning. This adds a further motivation for returning to this topic at this point.

Grammar induction was the subject of an intense study in the early days of Computational Learning Theory, with the theory of query learning largely developing out of this research. More recently the study of new methods of representing language and grammars through complex kernels and probabilistic modelling together with algorithms such as structured output learning has enabled machine learning methods to be applied successfully to a range of language related tasks from simple topic classification through parts of speech tagging to statistical machine translation. These methods typically rely on more fluid structures than those derived from formal grammars and yet are able to compete favourably with classical grammatical approaches that require significant input from domain experts, often in the form of annotated data.

Author Information

Alex Clark (Royal Holloway University of London)
Dorota Glowacka (University of Helsinki)
John Shawe-Taylor (UCL)

John Shawe-Taylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.

Yee Whye Teh (University of Oxford, DeepMind)

I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at DeepMind. I am also an Alan Turing Institute Fellow and a European Research Council Consolidator Fellow. I obtained my Ph.D. at the University of Toronto (working with Geoffrey Hinton), and did postdoctoral work at the University of California at Berkeley (with Michael Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). I was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL, and a tutorial fellow at University College Oxford, prior to my current appointment. I am interested in the statistical and computational foundations of intelligence, and works on scalable machine learning, probabilistic models, Bayesian nonparametrics and deep learning. I was programme co-chair of ICML 2017 and AISTATS 2010.

Chris J Watkins (Royal Holloway University of London)

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