1,392 research outputs found
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
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
ANNOTATED DISJUNCT FOR MACHINE TRANSLATION
Most information found in the Internet is available in English version. However,
most people in the world are non-English speaker. Hence, it will be of great advantage
to have reliable Machine Translation tool for those people. There are many
approaches for developing Machine Translation (MT) systems, some of them are
direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses
on developing an MT for less resourced languages i.e. languages that do not have
available grammar formalism, parser, and corpus, such as some languages in South
East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer
approaches. Moreover, the unavailability of grammar formalism and parser in the
target languages motivates us to develop a hybrid between direct and transfer
approaches. This hybrid approach is referred as a hybrid transfer approach. This
approach uses the Annotated Disjunct (ADJ) method. This method, based on Link
Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and
many-to-many word(s) translations. This method consists of transfer rules module
which maps source words in a source sentence (SS) into target words in correct
position in a target sentence (TS). The developed transfer rules are demonstrated on
English → Indonesian translation tasks. An experimental evaluation is conducted to
measure the performance of the developed system over available English-Indonesian
MT systems. The developed ADJ-based MT system translated simple, compound, and
complex English sentences in present, present continuous, present perfect, past, past
perfect, and future tenses with better precision than other systems, with the accuracy
of 71.17% in Subjective Sentence Error Rate metric
A Formal Model of Ambiguity and its Applications in Machine Translation
Systems that process natural language must cope with and resolve ambiguity. In this dissertation, a model of language processing is advocated in which multiple inputs and multiple analyses of inputs are considered concurrently and a single analysis is only a last resort. Compared to conventional models, this approach can be understood as replacing single-element inputs and outputs with weighted sets of inputs and outputs. Although processing components must deal with sets (rather than individual elements), constraints are imposed on the elements of these sets, and the representations from existing models may be reused. However, to deal efficiently with large (or infinite) sets, compact representations of sets that share structure between elements, such as weighted finite-state transducers and synchronous context-free grammars, are necessary. These representations and algorithms for manipulating them are discussed in depth in depth.
To establish the effectiveness and tractability of the proposed processing model, it is applied to several problems in machine translation. Starting with spoken language translation, it is shown that translating a set of transcription hypotheses yields better translations compared to a baseline in which a single (1-best) transcription hypothesis is selected and then translated, independent of the translation model formalism used. More subtle forms of ambiguity that arise even in text-only translation (such as decisions conventionally made during system development about how to preprocess text) are then discussed, and it is shown that the ambiguity-preserving paradigm can be employed in these cases as well, again leading to improved translation quality. A model for supervised learning that learns from training data where sets (rather than single elements) of correct labels are provided for each training instance and use it to learn a model of compound word segmentation is also introduced, which is used as a preprocessing step in machine translation
Resourcing machine translation with parallel treebanks
The benefits of syntax-based approaches to data-driven machine translation (MT) are clear: given the right model, a combination of hierarchical structure, constituent labels and morphological information can be exploited to produce more fluent, grammatical translation output. This has been demonstrated by the recent shift in research focus towards such linguistically motivated approaches. However, one issue facing developers of such models that is not encountered in the development of state-of-the-art string-based statistical MT (SMT) systems is the lack of available syntactically annotated training data for many languages.
In this thesis, we propose a solution to the problem of limited resources for syntax-based MT by introducing a novel sub-sentential alignment algorithm for the induction of translational equivalence links between pairs of phrase structure trees. This algorithm, which operates on a language pair-independent basis, allows for the automatic generation of large-scale parallel treebanks which are useful not only for machine translation, but also across a variety of natural language processing tasks. We demonstrate the viability of our automatically generated parallel treebanks by means of a thorough evaluation process during which they are compared to a manually annotated gold standard parallel treebank both intrinsically and in an MT task.
Following this, we hypothesise that these parallel treebanks are not only useful in syntax-based MT, but also have the potential to be exploited in other paradigms of MT. To this end, we carry out a large number of experiments across a variety of data sets and language pairs, in which we exploit the information encoded within the parallel treebanks in various components of phrase-based statistical MT systems. We demonstrate that improvements in translation accuracy can be achieved by enhancing SMT phrase tables with linguistically motivated phrase pairs extracted from a parallel treebank, while showing that a number of other features in SMT can also be supplemented with varying degrees of effectiveness. Finally, we examine ways in which synchronous grammars extracted from parallel treebanks can improve the quality of translation output, focussing on real translation examples from a syntax-based MT system
CCG-augmented hierarchical phrase-based statistical machine translation
Augmenting Statistical Machine Translation (SMT) systems with syntactic information aims at improving translation quality. Hierarchical Phrase-Based (HPB) SMT takes a step toward incorporating syntax in Phrase-Based (PB) SMT by modelling one aspect of language syntax, namely the hierarchical structure of phrases. Syntax Augmented Machine Translation (SAMT) further incorporates syntactic information extracted using context free phrase structure grammar (CF-PSG) in the HPB SMT model. One of the main challenges facing CF-PSG-based augmentation approaches for SMT systems emerges from the difference in the definition of the constituent in CF-PSG and the ‘phrase’ in SMT systems, which hinders the ability of CF-PSG to express the syntactic function of many SMT phrases. Although the SAMT approach to solving this problem using ‘CCG-like’ operators to combine constituent labels improves syntactic constraint coverage, it significantly increases their sparsity, which restricts translation and negatively affects its quality.
In this thesis, we address the problems of sparsity and limited coverage of syntactic constraints facing the CF-PSG-based syntax augmentation approaches for HPB SMT using Combinatory Cateogiral Grammar (CCG). We demonstrate that
CCG’s flexible structures and rich syntactic descriptors help to extract richer, more expressive and less sparse syntactic constraints with better coverage than CF-PSG,
which enables our CCG-augmented HPB system to outperform the SAMT system. We also try to soften the syntactic constraints imposed by CCG category nonterminal labels by extracting less fine-grained CCG-based labels. We demonstrate that CCG label simplification helps to significantly improve the performance of our CCG category HPB system. Finally, we identify the factors which limit the coverage of the syntactic constraints in our CCG-augmented HPB model. We then try to tackle these factors by extending the definition of the nonterminal label to be composed of a sequence of CCG categories and augmenting the glue grammar with CCG combinatory rules. We demonstrate that our extension approaches help to significantly increase the scope of the syntactic constraints applied in our CCG-augmented HPB model and achieve significant improvements over the HPB SMT baseline
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