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Title: Structured learning with inexact search : advances in shift-reduce CCG parsing
Author: Xu, Wenduan
ISNI:       0000 0004 7225 4552
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2017
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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.
Supervisor: Clark, Stephen Sponsor: Carnegie Trust for the Universities of Scotland ; Cambridge Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Structured Prediction ; Structured Learning with Inexact Search ; Violation-Fixing Structured Perceptron ; Recurrent Neural Networks ; LSTMs ; Combinatory Categorial Grammar ; Shift-Reduce Transition-based Parsing