374 research outputs found
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
An empirical analysis of phrase-based and neural machine translation
Two popular types of machine translation (MT) are phrase-based and neural
machine translation systems. Both of these types of systems are composed of
multiple complex models or layers. Each of these models and layers learns
different linguistic aspects of the source language. However, for some of these
models and layers, it is not clear which linguistic phenomena are learned or
how this information is learned. For phrase-based MT systems, it is often clear
what information is learned by each model, and the question is rather how this
information is learned, especially for its phrase reordering model. For neural
machine translation systems, the situation is even more complex, since for many
cases it is not exactly clear what information is learned and how it is
learned.
To shed light on what linguistic phenomena are captured by MT systems, we
analyze the behavior of important models in both phrase-based and neural MT
systems. We consider phrase reordering models from phrase-based MT systems to
investigate which words from inside of a phrase have the biggest impact on
defining the phrase reordering behavior. Additionally, to contribute to the
interpretability of neural MT systems we study the behavior of the attention
model, which is a key component in neural MT systems and the closest model in
functionality to phrase reordering models in phrase-based systems. The
attention model together with the encoder hidden state representations form the
main components to encode source side linguistic information in neural MT. To
this end, we also analyze the information captured in the encoder hidden state
representations of a neural MT system. We investigate the extent to which
syntactic and lexical-semantic information from the source side is captured by
hidden state representations of different neural MT architectures.Comment: PhD thesis, University of Amsterdam, October 2020.
https://pure.uva.nl/ws/files/51388868/Thesis.pd
The impact of source-side syntactic reordering on hierarchical phrase-based SMT
Syntactic reordering has been demonstrated
to be helpful and effective for handling
different word orders between source
and target languages in SMT. However, in
terms of hierarchial PB-SMT (HPB), does
the syntactic reordering still has a significant
impact on its performance? This
paper introduces a reordering approach
which explores the { (DE) grammatical
structure in Chinese. We employ
the Stanford DE classifier to recognise
the DE structures in both training and
test sentences of Chinese, and then perform
word reordering to make the Chinese
sentences better match the word order
of English. The annotated and reordered
training data and test data are applied
to a re-implemented HPB system and
the impact of the DE construction is examined.
The experiments are conducted
on the NIST 2008 evaluation data and experimental
results show that the BLEU
and METEOR scores are significantly improved
by 1.83/8.91 and 1.17/2.73 absolute/
relative points respectively
Coupling hierarchical word reordering and decoding in phrase-based statistical machine translation
In this paper, we start with the existing idea of taking reordering rules automatically derived from syntactic representations, and applying them in a preprocessing step before translation to make the source sentence structurally more like the target; and we propose a new approach to hierarchically extracting these rules. We evaluate this, combined with a lattice-based decoding, and show improvements over stateof-the-art distortion models.Postprint (published version
How much hybridisation does machine translation need?
This is the peer reviewed version of the following article: [Costa-jussà, M. R. (2015), How much hybridization does machine translation Need?. J Assn Inf Sci Tec, 66: 2160–2165. doi:10.1002/asi.23517], which has been published in final form at [10.1002/asi.23517]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Rule-based and corpus-based machine translation (MT)have coexisted for more than 20 years. Recently, bound-aries between the two paradigms have narrowed andhybrid approaches are gaining interest from bothacademia and businesses. However, since hybridapproaches involve the multidisciplinary interaction oflinguists, computer scientists, engineers, and informa-tion specialists, understandably a number of issuesexist.While statistical methods currently dominate researchwork in MT, most commercial MT systems are techni-cally hybrid systems. The research community shouldinvestigate the bene¿ts and questions surrounding thehybridization of MT systems more actively. This paperdiscusses various issues related to hybrid MT includingits origins, architectures, achievements, and frustra-tions experienced in the community. It can be said thatboth rule-based and corpus- based MT systems havebene¿ted from hybridization when effectively integrated.In fact, many of the current rule/corpus-based MTapproaches are already hybridized since they do includestatistics/rules at some point.Peer ReviewedPostprint (author's final draft
Linguistic Structure in Statistical Machine Translation
This thesis investigates the influence of linguistic structure in statistical machine translation. We develop a word reordering model based on syntactic parse trees and address the issues of pronouns and morphological agreement with a source discriminative word lexicon predicting the translation for individual words using structural features. When used in phrase-based machine translation, the models improve the translation for language pairs with different word order and morphological variation
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