STRUCTURED LEARNING WITH INEXACT SEARCH: ADVANCES IN SHIFT-REDUCE CCG PARSING
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Authors
Xu, Wenduan
Advisors
Clark, Stephen
Date
2017-12-01Awarding Institution
University of Cambridge
Author Affiliation
Department of Computer Science and Technology -- the Computer Laboratory
Qualification
Doctor of Philosophy (PhD)
Language
English
Type
Thesis
Metadata
Show full item recordCitation
Xu, W. (2017). STRUCTURED LEARNING WITH INEXACT SEARCH: ADVANCES IN SHIFT-REDUCE CCG PARSING (Doctoral thesis). https://doi.org/10.17863/CAM.16866
Abstract
Statistical shift-reduce parsing involves the interplay of representation learning, structured learning, and inexact search. This dissertation considers approaches that tightly integrate these three elements and explores three novel models for shift-reduce CCG parsing. First, I develop a dependency model, in which the selection of shift-reduce action sequences producing a dependency structure is treated as a hidden variable; the key components of the model are a dependency oracle and a learning algorithm that integrates the dependency oracle, the structured perceptron, and beam search. Second, I present expected F-measure training and show how to derive a globally normalized RNN model, in which beam search is naturally incorporated and used in conjunction with the
objective to learn shift-reduce action sequences optimized for the final evaluation metric. Finally, I describe an LSTM model that is able to construct parser state representations incrementally by following the shift-reduce syntactic derivation process; I show expected F-measure training, which is agnostic to the underlying neural network, can be applied in this setting to obtain globally normalized greedy and beam-search LSTM shift-reduce parsers.
Keywords
Structured Prediction, Structured Learning with Inexact Search, Violation-Fixing Structured Perceptron, Recurrent Neural Networks, LSTMs, Combinatory Categorial Grammar, Shift-Reduce Transition-based Parsing
Sponsorship
The Carnegie Trust for the Universities of Scotland
The Cambridge Trust
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.16866
Rights
No Creative Commons licence (All rights reserved)