5 research outputs found
A Hybrid Machine Translation Framework for an Improved Translation Workflow
Over the past few decades, due to a continuing surge in the amount of content being translated and ever increasing pressure to deliver high quality and high throughput translation, translation industries are focusing their interest on adopting advanced technologies such as machine translation (MT), and automatic post-editing (APE) in their translation workflows. Despite the progress of the technology, the roles of humans and machines essentially remain intact as MT/APE are moving from the peripheries of the translation field closer towards collaborative human-machine based MT/APE in modern translation workflows. Professional translators increasingly become post-editors correcting raw MT/APE output instead of translating from scratch which in turn increases productivity in terms of translation speed. The last decade has seen substantial growth in research and development activities on improving MT; usually concentrating on selected aspects of workflows starting from training data pre-processing techniques to core MT processes to post-editing methods. To date, however, complete MT workflows are less investigated than the core MT processes. In the research presented in this thesis, we investigate avenues towards achieving improved MT workflows. We study how different MT paradigms can be utilized and integrated to best effect. We also investigate how different upstream and downstream component technologies can be hybridized to achieve overall improved MT. Finally we include an investigation into human-machine collaborative MT by taking humans in the loop. In many of (but not all) the experiments presented in this thesis we focus on data scenarios provided by low resource language settings.Aufgrund des stetig ansteigenden Übersetzungsvolumens in den letzten Jahrzehnten und
gleichzeitig wachsendem Druck hohe Qualität innerhalb von kürzester Zeit liefern zu
müssen sind Übersetzungsdienstleister darauf angewiesen, moderne Technologien wie
Maschinelle Übersetzung (MT) und automatisches Post-Editing (APE) in den Übersetzungsworkflow
einzubinden. Trotz erheblicher Fortschritte dieser Technologien haben
sich die Rollen von Mensch und Maschine kaum verändert. MT/APE ist jedoch nunmehr
nicht mehr nur eine Randerscheinung, sondern wird im modernen Übersetzungsworkflow
zunehmend in Zusammenarbeit von Mensch und Maschine eingesetzt. Fachübersetzer
werden immer mehr zu Post-Editoren und korrigieren den MT/APE-Output, statt wie
bisher Übersetzungen komplett neu anzufertigen. So kann die Produktivität bezüglich
der Übersetzungsgeschwindigkeit gesteigert werden. Im letzten Jahrzehnt hat sich in den
Bereichen Forschung und Entwicklung zur Verbesserung von MT sehr viel getan: Einbindung
des vollständigen Übersetzungsworkflows von der Vorbereitung der Trainingsdaten
über den eigentlichen MT-Prozess bis hin zu Post-Editing-Methoden. Der vollständige
Übersetzungsworkflow wird jedoch aus Datenperspektive weit weniger berücksichtigt
als der eigentliche MT-Prozess. In dieser Dissertation werden Wege hin zum
idealen oder zumindest verbesserten MT-Workflow untersucht. In den Experimenten
wird dabei besondere Aufmertsamfit auf die speziellen Belange von sprachen mit geringen
ressourcen gelegt. Es wird untersucht wie unterschiedliche MT-Paradigmen verwendet
und optimal integriert werden können. Des Weiteren wird dargestellt wie unterschiedliche
vor- und nachgelagerte Technologiekomponenten angepasst werden können, um insgesamt
einen besseren MT-Output zu generieren. Abschließend wird gezeigt wie der Mensch in
den MT-Workflow intergriert werden kann. Das Ziel dieser Arbeit ist es verschiedene
Technologiekomponenten in den MT-Workflow zu integrieren um so einen verbesserten
Gesamtworkflow zu schaffen. Hierfür werden hauptsächlich Hybridisierungsansätze verwendet.
In dieser Arbeit werden außerdem Möglichkeiten untersucht, Menschen effektiv
als Post-Editoren einzubinden
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
Mono- and cross-lingual paraphrased text reuse and extrinsic plagiarism detection
Text reuse is the act of borrowing text (either verbatim or paraphrased) from an earlier written text. It could occur within the same language (mono-lingual) or across languages (cross-lingual) where the reused text is in a different language than the original text. Text reuse and its related problem, plagiarism (the unacknowledged reuse of text), are becoming serious issues in many fields and research shows that paraphrased and especially the cross-lingual cases of reuse are much harder to detect. Moreover, the recent rise in readily available multi-lingual content on the Web and social media has increased the problem to an unprecedented scale. To develop, compare, and evaluate automatic methods for mono- and crosslingual text reuse and extrinsic (finding portion(s) of text that is reused from the original text) plagiarism detection, standard evaluation resources are of utmost importance. However, previous efforts on developing such resources have mostly focused on English and some other languages. On the other hand, the Urdu language, which is widely spoken and has a large digital footprint, lacks resources in terms of core language processing tools and corpora. With this consideration in mind, this PhD research focuses on developing standard evaluation corpora, methods, and supporting resources to automatically detect mono-lingual (Urdu) and cross-lingual (English-Urdu) cases of text reuse and extrinsic plagiarism This thesis contributes a mono-lingual (Urdu) text reuse corpus (COUNTER Corpus) that contains real cases of Urdu text reuse at document-level. Another contribution is the development of a mono-lingual (Urdu) extrinsic plagiarism corpus (UPPC Corpus) that contains simulated cases of Urdu paraphrase plagiarism. Evaluation results, by applying a wide range of state-of-the-art mono-lingual methods on both corpora, shows that it is easier to detect verbatim cases than paraphrased ones. Moreover, the performance of these methods decreases considerably on real cases of reuse. A couple of supporting resources are also created to assist methods used in the cross-lingual (English-Urdu) text reuse detection. A large-scale multi-domain English-Urdu parallel corpus (EUPC-20) that contains parallel sentences is mined from the Web and several bi-lingual (English-Urdu) dictionaries are compiled using multiple approaches from different sources. Another major contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus (TREU Corpus). It contains English to Urdu real cases of text reuse at the document-level. A diversified range of methods are applied on the TREU Corpus to evaluate its usefulness and to show how it can be utilised in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse. A new cross-lingual method is also proposed that uses bilingual word embeddings to estimate the degree of overlap amongst text documents by computing the maximum weighted cosine similarity between word pairs. The overall low evaluation results indicate that it is a challenging task to detect crosslingual real cases of text reuse, especially when the language pairs have unrelated scripts, i.e., English-Urdu. However, an improvement in the result is observed using a combination of methods used in the experiments. The research work undertaken in this PhD thesis contributes corpora, methods, and supporting resources for the mono- and cross-lingual text reuse and extrinsic plagiarism for a significantly under-resourced Urdu and English-Urdu language pair. It highlights that paraphrased and cross-lingual cross-script real cases of text reuse are harder to detect and are still an open issue. Moreover, it emphasises the need to develop standard evaluation and supporting resources for under-resourced languages to facilitate research in these languages. The resources that have been developed and methods proposed could serve as a framework for future research in other languages and language pairs