1,053 research outputs found
Constructing a Large-Scale English-Persian Parallel Corpus
In recent years the exploitation of large text corpora in solving various kinds of linguistic problems, including those of translation, is commonplace. Yet a large-scale English-Persian corpus is still unavailable, because of certain difficulties and the amount of work required to overcome them.The project reported here is an attempt to constitute an English-Persian parallel corpus composed of digital texts and Web documents containing little or no noise. The Internet is useful because translations of existing texts are often published on the Web. The task is to find parallel pages in English and Persian, to judge their translation quality, and to download and align them. The corpus so created is of course open; that is, more material can be added as the need arises.One of the main activities associated with building such a corpus is to develop software for parallel concordancing, in which a user can enter a search string in one language and see all the citations for that string in it and corresponding sentences in the target language. Our intention is to construct general translation memory software using the present English-Persian parallel corpus.Au cours des dernières années, l’exploitation de grands corpus de textes pour résoudre des problèmes linguistiques, notamment des problèmes de traduction, est devenue une pratique courante. Jusqu’à récemment, aucun corpus bilingue anglais-persan à grande échelle n’avait été constitué, en raison des difficultés qu’implique une telle entreprise.Cet article présente un projet réalisé en vue de colliger des corpus de textes numériques variés, tels que des documents du réseau Internet, avec le moins de bruit possible. L’utilisation d’Internet peut être considérée comme une aide précieuse car, souvent, il existe des traductions antérieures qui sont déjà publiées sur le Web. La tâche consiste à trouver les pages parallèles en anglais et en persan, à évaluer la qualité de leur traduction, à les télécharger et à les aligner. Le corpus ainsi obtenu est un corpus ouvert, soit un corpus auquel de nouvelles données peuvent être ajoutées, selon les besoins.Une des principales conséquences de l’élaboration d’un tel corpus est la mise au point d’un logiciel de concordance parallèle, dans lequel l’utilisateur pourrait introduire une chaîne de caractères dans une langue et afficher toutes les citations concernant cette chaîne dans la langue recherchée ainsi que des phrases correspondantes dans la langue cible. L’étape suivante serait d’utiliser ce corpus parallèle pour construire un logiciel de traduction générale.Le corpus bilingue aligné se trouve être utile dans beaucoup d’autres cas, entre autres pour la traduction par ordinateur, pour lever les ambiguïtés de sens, pour le rétablissement des données interlangues, en lexicographie ainsi que pour l’apprentissage des langues
A Hybrid Accurate Alignment method for large Persian-English corpus construction based on statistical analysis and Lexicon/Persian Word net
A bilingual corpus is considered as a very important knowledge source and an inevitable requirement for many natural language processing (NLP) applications in which two languages are involved. For some languages such as Persian, lack of such resources is much more significant. Several applications, including statistical and example-based machine translation needs bilingual corpora, in which large amounts of texts from two different languages have been aligned at the sentence or phrase levels. In order to meet this requirement, this paper aims to propose an accurate and hybrid sentence alignment method for construction of an English-Persian parallel corpus. As the first step, the proposed method uses statistical length based analysis for filtering of candidates. Punctuation marks are used as a directing feature to reduce the complexity and increase the accuracy. Finally, the proposed method makes use of some lexical knowledge in order to produce the final output. . In the phase of lexical analysis, a bilingual dictionary as well as a Persian semantic net (denoted as FarsNet) is used to calculate the extended semantic similarity. Experiments showed the positive effect of expansion on synonym words by extended semantic similarity on the accuracy of the sentence alignment process. In the proposed matching scheme, a semantic load based approach (which considers the verb as the pivot and the main part of a sentence) was also used in order for increasing the accuracy. The results obtained from the experiments were promising and the generated parallel corpus can be used as an effective knowledge source by researchers who work on Persian language
Translation Alignment Applied to Historical Languages: methods, evaluation, applications, and visualization
Translation alignment is an essential task in Digital Humanities and Natural
Language Processing, and it aims to link words/phrases in the source
text with their translation equivalents in the translation. In addition to
its importance in teaching and learning historical languages, translation
alignment builds bridges between ancient and modern languages through
which various linguistics annotations can be transferred. This thesis focuses
on word-level translation alignment applied to historical languages in general
and Ancient Greek and Latin in particular. As the title indicates, the thesis
addresses four interdisciplinary aspects of translation alignment.
The starting point was developing Ugarit, an interactive annotation tool
to perform manual alignment aiming to gather training data to train an
automatic alignment model. This effort resulted in more than 190k accurate
translation pairs that I used for supervised training later. Ugarit has been
used by many researchers and scholars also in the classroom at several
institutions for teaching and learning ancient languages, which resulted
in a large, diverse crowd-sourced aligned parallel corpus allowing us to
conduct experiments and qualitative analysis to detect recurring patterns in
annotators’ alignment practice and the generated translation pairs.
Further, I employed the recent advances in NLP and language modeling to
develop an automatic alignment model for historical low-resourced languages,
experimenting with various training objectives and proposing a training
strategy for historical languages that combines supervised and unsupervised
training with mono- and multilingual texts. Then, I integrated this alignment
model into other development workflows to project cross-lingual annotations
and induce bilingual dictionaries from parallel corpora.
Evaluation is essential to assess the quality of any model. To ensure employing the best practice, I reviewed the current evaluation procedure, defined
its limitations, and proposed two new evaluation metrics. Moreover, I introduced a visual analytics framework to explore and inspect alignment gold
standard datasets and support quantitative and qualitative evaluation of
translation alignment models. Besides, I designed and implemented visual
analytics tools and reading environments for parallel texts and proposed
various visualization approaches to support different alignment-related tasks
employing the latest advances in information visualization and best practice.
Overall, this thesis presents a comprehensive study that includes manual and
automatic alignment techniques, evaluation methods and visual analytics
tools that aim to advance the field of translation alignment for historical
languages
MultiWiki: interlingual text passage alignment in Wikipedia
In this article we address the problem of text passage alignment across interlingual article pairs in Wikipedia. We develop methods that enable the identification and interlinking of text passages written in different languages and containing overlapping information. Interlingual text passage alignment can enable Wikipedia editors and readers to better understand language-specific context of entities, provide valuable insights in cultural differences and build a basis for qualitative analysis of the articles. An important challenge inthis context is the trade-off between the granularity of the extracted text passages and the precision of the alignment. Whereas short text passages can result in more precise alignment, longer text passages can facilitate a better overview of the differences in an article pair. To better understand these aspects from the user perspective, we conduct a user study at the example of the German, Russian and the English Wikipedia and collect a user-annotated benchmark. Then we propose MultiWiki – a method that adopts an integrated approach to the text passage alignment using semantic similarity measures and greedy algorithms and achieves precise results with respect to the user-defined alignment. MultiWiki demonstration is publicly available and currently supports four language pairs
Reordering in statistical machine translation
PhDMachine translation is a challenging task that its difficulties arise from several characteristics
of natural language. The main focus of this work is on reordering as one of
the major problems in MT and statistical MT, which is the method investigated in this
research. The reordering problem in SMT originates from the fact that not all the words
in a sentence can be consecutively translated. This means words must be skipped and
be translated out of their order in the source sentence to produce a fluent and grammatically
correct sentence in the target language. The main reason that reordering is
needed is the fundamental word order differences between languages. Therefore, reordering
becomes a more dominant issue, the more source and target languages are
structurally different.
The aim of this thesis is to study the reordering phenomenon by proposing new methods
of dealing with reordering in SMT decoders and evaluating the effectiveness of
the methods and the importance of reordering in the context of natural language processing
tasks. In other words, we propose novel ways of performing the decoding to
improve the reordering capabilities of the SMT decoder and in addition we explore
the effect of improving the reordering on the quality of specific NLP tasks, namely
named entity recognition and cross-lingual text association. Meanwhile, we go beyond
reordering in text association and present a method to perform cross-lingual text fragment
alignment, based on models of divergence from randomness.
The main contribution of this thesis is a novel method named dynamic distortion,
which is designed to improve the ability of the phrase-based decoder in performing
reordering by adjusting the distortion parameter based on the translation context. The
model employs a discriminative reordering model, which is combining several fea-
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tures including lexical and syntactic, to predict the necessary distortion limit for each
sentence and each hypothesis expansion. The discriminative reordering model is also
integrated into the decoder as an extra feature. The method achieves substantial improvements
over the baseline without increase in the decoding time by avoiding reordering
in unnecessary positions.
Another novel method is also presented to extend the phrase-based decoder to dynamically
chunk, reorder, and apply phrase translations in tandem. Words inside the chunks
are moved together to enable the decoder to make long-distance reorderings to capture
the word order differences between languages with different sentence structures.
Another aspect of this work is the task-based evaluation of the reordering methods and
other translation algorithms used in the phrase-based SMT systems. With more successful
SMT systems, performing multi-lingual and cross-lingual tasks through translating
becomes more feasible. We have devised a method to evaluate the performance
of state-of-the art named entity recognisers on the text translated by a SMT decoder.
Specifically, we investigated the effect of word reordering and incorporating reordering
models in improving the quality of named entity extraction.
In addition to empirically investigating the effect of translation in the context of crosslingual
document association, we have described a text fragment alignment algorithm
to find sections of the two documents in different languages, that are content-wise related.
The algorithm uses similarity measures based on divergence from randomness
and word-based translation models to perform text fragment alignment on a collection
of documents in two different languages.
All the methods proposed in this thesis are extensively empirically examined. We have
tested all the algorithms on common translation collections used in different evaluation
campaigns. Well known automatic evaluation metrics are used to compare the
suggested methods to a state-of-the art baseline and results are analysed and discussed
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