DS-2012-02: Learning the Latent Structure of Translation

DS-2012-02: Mylonakis, Markos (2012) Learning the Latent Structure of Translation. Doctoral thesis, University of Amsterdam.

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This dissertation discusses methods to learn the latent structural
patterns that underlie translation data. It explores different
approaches to modelling bilingual structure and presents novel
frameworks and algorithms, such as Cross-Validated
Expectation-Maximization (CV-EM), to learn phrase-based, hierarchical
and syntax-driven Statistical Machine Translation (SMT) models from

In this thesis, we present methods to automatically learn phrase-based
Statistical Machine Translation models that assume a latent bilingual
structure as their central modelling variable. Acknowledging that
each language is strongly characterised by its individual structural
properties, we aim to learn a bilingual structure that augments and
supersedes its monolingual counterparts, to bridge the gap between
them by explaining the transformations taking place when conveying
meaning across languages. The learning frameworks and algorithms we
present allow us to discover these structural patterns in bilingual
data and automatically learn models that take them into account to
better translate. We apply our methodology for a sequence of
statistical translation models of increasing complexity. This leads
us to the presentation of a well-founded learning framework for
hierarchical, syntactically motivated models that explain the
translation process by taking advantage of the linguistic structure of

Chapter 1 offers an introduction to the context and aims of this work.
It introduces the key aspects related to modelling translation
structure and discusses the impact of its latent nature, as well as
the challenges involved in learning to identify it in bilingual data.
In Chapter 2, we start by examining some of the modelling frameworks
that have been influential on SMT research, such as word-based,
phrase-based and hierarchical SMT. We then discuss the EM algorithm
and Cross-Validation, the two theoretical pillars under the novel
learning algorithm we introduce in the chapter that follows. Chapter
3 examines the challenges related to learning phrase-based translation
models, by considering the wider problem of learning Fragment Models:
models which describe how to build new data instances by combining
together data fragments extracted from a training dataset. We then
introduce the Cross-Validated Expectation-Maximization (CV-EM)
algorithm, a novel learning algorithm for Fragment Models which
optimises parameters according to a Cross-Validated Maximum Likelihood
Estimation (CV-MLE) objective.

The next three chapters describe and empirically evaluate learning
frameworks with CV-EM at their core, for three distinct,
state-of-the-art SMT models. Chapter 4 contributes a well-founded
method to learn the conditional translation probabilities of
Phrase-Based SMT models employing contiguous phrase-pairs, centred
around disambiguating the latent segmentation of sentence-pairs into
phrase-pairs. This method is shown empirically to perform at least as
well as the heuristic, ad hoc estimators that are typically used for
these models. In Chapter 5, we consider the additional challenges
involved in modelling translation with a synchronous grammar, and
successfully learn a relatively simple hierarchical translation model
which offers comparable performance with a highly competitive
baseline. Chapter 6 moves considerably further, to build around CV-EM
a learning framework that allows learning complex hierarchical
translation models that take advantage of external annotations of
source and/or target sentences. We deploy this framework to
contribute a method to learn linguistically motivated hierarchical
translation models, by identifying the source-language linguistic
patterns which are informative for translation. We subsequently show
how our approach delivers tangible translation improvements across
four distinct language pairs.

The results of Chapter 6 complete those of Chapters 4 and 5, to
provide considerable evidence to back the key hypothesis of this
thesis: models assuming a latent translation structure can be learnt
under a clear learning objective, as implemented in terms of a
well-understood optimisation framework and learning algorithm. The
learnt models are able to provide real-world, competitive translation
performance in comparison to heuristic training regimes, rendering the
use of the latter unnecessary. Our methodology not only provides a
reliable and effective substitute for these heuristic estimators, but
most importantly lays a path to the future, by making possible the
estimation of powerful translation models that uncover the latent side
of translation, and whose estimation under ad hoc algorithms would
have been hardly possible.

Item Type: Thesis (Doctoral)
Report Nr: DS-2012-02
Series Name: ILLC Dissertation (DS) Series
Year: 2012
Subjects: Language
Depositing User: Dr Marco Vervoort
Date Deposited: 14 Jun 2022 15:16
Last Modified: 14 Jun 2022 15:16
URI: https://eprints.illc.uva.nl/id/eprint/2108

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