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

Terminology Integration in Statistical Machine Translation

Elektroniskā versija nesatur pielikumusPromocijas darbs apraksta autora izpētītas metodes un izstrādātus rīkus divvalodu terminoloģijas integrācijai statistiskās mašīntulkošanas sistēmās. Autors darbā piedāvā inovatīvas metodes terminu integrācijai SMT sistēmu trenēšanas fāzē (ar statiskas integrācijas palīdzību) un tulkošanas fāzē (ar dinamiskas integrācijas palīdzību). Darbā uzmanība pievērsta ne tikai metodēm terminu integrācijai SMT, bet arī metodēm valodas resursu, kas nepieciešami dažādu uzdevumu veikšanai terminu integrācijas SMT darbplūsmās, ieguvei. Piedāvātās metodes ir novērtētas automātiskas un manuālas novērtēšanas eksperimentos. Iegūtie rezultāti parāda, ka statiskās un dinamiskās integrācijas metodes ļauj būtiski uzlabot tulkošanas kvalitāti. Darbā aprakstītie rezultāti ir aprobēti vairākos pētniecības projektos un ieviesti praktiskos risinājumos. Atslēgvārdi: statistiskā mašīntulkošana, terminoloģija, starpvalodu informācijas izvilkšanaThe doctoral thesis describes methods and tools researched and developed by the author for bilingual terminology integration into statistical machine translation systems. The author presents novel methods for terminology integration in SMT systems during training (through static integration) and during translation (through dynamic integration). The work focusses not only on the SMT integration techniques, but also on methods for acquisition of linguistic resources that are necessary for different tasks involved in workflows for terminology integration in SMT systems. The proposed methods have been evaluated using automatic and manual evaluation methods. The results show that both static and dynamic integration methods allow increasing translation quality. The thesis describes also areas where the methods have been approbated in practice. Keywords: statistical machine translation, terminology, cross-lingual information extractio

On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism

Barrón Cedeño, LA. (2012). On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16012Palanci

Learning Chinese language structures with multiple views

Motivated by the inadequacy of single view approaches in many areas in NLP, we study multi-view Chinese language processing, including word segmentation, part-of-speech (POS) tagging, syntactic parsing and semantic role labeling (SRL), in this thesis. We consider three situations of multiple views in statistical NLP: (1) Heterogeneous computational models have been designed for a given problem; (2) Heterogeneous annotation data is available to train systems; (3) Supervised and unsupervised machine learning techniques are applicable. First, we comparatively analyze successful single view approaches for Chinese lexical, syntactic and semantic processing. Our analysis highlights the diversity between heterogenous systems built on different views, and motivates us to improve the state-of-the-art by combining or integrating heterogeneous approaches. Second, we study the annotation ensemble problem, i.e. learning from multiple data sets under different annotation standards. We propose a series of generalized stacking models to effectively utilize heterogeneous labeled data to reduce approximation errors for word segmentation and parsing. Finally, we are concerned with bridging the gap between unsupervised and supervised learning paradigms. We introduce feature induction solutions that harvest useful linguistic knowledge from large-scale unlabeled data and effectively use them as new features to enhance discriminative learning based systems. For word segmentation, we present a comparative study of word-based and character-based approaches. Inspired by the diversity of the two views, we design a novel stacked sub-word tagging model for joint word segmentation and POS tagging, which is robust to integrate different models, even models trained on heterogeneous annotations. To benefit from unsupervised word segmentation, we derive expressive string knowledge from unlabeled data which significantly enhances a strong supervised segmenter. For POS tagging, we introduce two linguistically motivated improvements: (1) combining syntax-free sequential tagging and syntax-based chart parsing results to better capture syntagmatic lexical relations and (2) integrating word clusters acquired from unlabeled data to better capture paradigmatic lexical relations. For syntactic parsing, we present a comparative analysis for generative PCFG-LA constituency parsing and discriminative graph-based dependency parsing. To benefit from the diversity of parsing in different formalisms, we implement a previously introduced stacking method and propose a novel Bagging model to combine complementary strengths of grammar-free and grammar-based models. In addition to the study on the syntactic formalism, we also propose a reranking model to explore heterogenous treebanks that are labeled under different annotation scheme. Finally, we continue our efforts on combining strengths of supervised and unsupervised learning, and evaluate the impact of word clustering on different syntactic processing tasks. Our work on SRL focus on improving the full parsing method with linguistically rich features and a chunking strategy. Furthermore, we developed a partial parsing based semantic chunking method, which has complementary strengths to the full parsing based method. Based on our work, Zhuang and Zong (2010) successfully improve the state-of-the-art by combining full and partial parsing based SRL systems.Motiviert durch die Unzulänglichkeit der Ansätze mit dem einzigen Ansicht in vielen Bereichen in NLP, untersuchen wir Chinesische Sprache Verarbeitung mit mehrfachen Ansichten, einschließlich Wortsegmentierung, Part-of-Speech (POS)-Tagging und syntaktische Parsing und die Kennzeichnung der semantische Rolle (SRL) in dieser Arbeit . Wir betrachten drei Situationen von mehreren Ansichten in der statistischen NLP: (1) Heterogene computergestützte Modelle sind für ein gegebenes Problem entwurft, (2) Heterogene Annotationsdaten sind verfügbar, um die Systeme zu trainieren, (3) überwachten und unüberwachten Methoden des maschinellen Lernens sind zur Verfügung gestellt. Erstens, wir analysieren vergleichsweise erfolgreiche Ansätze mit einzigen Ansicht für chinesische lexikalische, syntaktische und semantische Verarbeitung. Unsere Analyse zeigt die Unterschiede zwischen den heterogenen Systemen, die auf verschiedenen Ansichten gebaut werden, und motiviert uns, die state-of-the-Art durch die Kombination oder Integration heterogener Ansätze zu verbessern. Zweitens, untersuchen wir die Annotation Ensemble Problem, d.h. das Lernen aus mehreren Datensätzen unter verschiedenen Annotation Standards. Wir schlagen eine Reihe allgemeiner Stapeln Modelle, um eine effektive Nutzung heterogener Daten zu beschriften, und um Approximationsfehler für Wort Segmentierung und Analyse zu reduzieren. Schließlich sind wir besorgt mit der Überbrückung der Kluft zwischen unüberwachten und überwachten Lernens Paradigmen. Wir führen Induktion Feature-Lösungen, die nützliche Sprachkenntnisse von großflächigen unmarkierter Daten ernte, und die effektiv nutzen als neue Features, um die unterscheidenden Lernen basierten Systemen zu verbessern. Für die Wortsegmentierung, präsentieren wir eine vergleichende Studie der Wort-basierte und Charakter-basierten Ansätzen. Inspiriert von der Vielfalt der beiden Ansichten, entwerfen wir eine neuartige gestapelt Sub-Wort-Tagging-Modell für gemeinsame Wort-Segmentierung und POS-Tagging, die robust ist, um verschiedene Modelle zu integrieren, auch Modelle auf heterogenen Annotationen geschult. Um den unbeaufsichtigten Wortsegmentierung zu profitieren, leiten wir ausdrucksstarke Zeichenfolge Wissen von unmarkierten Daten. Diese Methode hat eine überwachte Methode erheblich verbessert. Für POS-Tagging, führen wir zwei linguistisch motiviert Verbesserungen: (1) die Kombination von Syntaxfreie sequentielle Tagging und Syntaxbasierten Grafik-Parsing-Ergebnisse, um syntagmatische lexikalische Beziehungen besser zu erfassen (2) die Integration von Wortclusteren von nicht markierte Daten, um die paradigmatische lexikalische Beziehungen besser zu erfassen. Für syntaktische Parsing präsentieren wir eine vergleichenbare Analyse für generative PCFG-LA Wahlkreis Parsing und diskriminierende Graphen-basierte Abhängigkeit Parsing. Um aus der Vielfalt der Parsen in unterschiedlichen Formalismen zu profitieren, setzen wir eine zuvor eingeführte Stacking-Methode und schlagen eine neuartige Schrumpfbeutel-Modell vor, um die ergänzenden Stärken der Grammatik und Grammatik-free-basierte Modelle zu kombinieren. Neben dem syntaktischen Formalismus, wir schlagen auch ein Modell, um heterogene reranking Baumbanken, die unter verschiedenen Annotationsschema beschriftet sind zu erkunden. Schließlich setzen wir unsere Bemühungen auf die Bündelung von Stärken des überwachten und unüberwachten Lernen, und bewerten wir die Auswirkungen der Wort-Clustering auf verschiedene syntaktische Verarbeitung Aufgaben. Unsere Arbeit an SRL ist konzentriert auf die Verbesserung der vollen Parsingsmethode mit linguistischen umfangreichen Funktionen und einer Chunkingstrategie. Weiterhin entwickelten wir eine semantische Chunkingmethode basiert auf dem partiellen Parsing, die die komplementäre Stärken gegen die die Methode basiert auf dem vollen Parsing hat. Basiert auf unserer Arbeit, Zhuang und Zong (2010) hat den aktuelle Stand erfolgreich verbessert durch die Kombination von voll-und partielle-Parsing basierte SRL Systeme

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

LOCATING AND REDUCING TRANSLATIONDIFFICULTY

The challenge of translation varies from one sentence to another, or even between phrases of a sentence. We investigate whether variations in difficulty can be located automatically for Statistical Machine Translation (SMT). Furthermore, we hypothesize that customization of a SMT system based on difficulty information, improves the translation quality.We assume a binary categorization for phrases: easy vs. difficult. Our focus is on the Difficult to Translate Phrases (DTPs). Our experiments show that for a sentence, improving the translation of the DTP improves the translation of the surrounding non-difficult phrases too. To locate the most difficult phrase of each sentence, we use machine learning and construct a difficulty classifier. To improve the translation of DTPs, we introduce customization methods for three components of the SMT system: I. language model; II. translation model; III. decoding weights. With each method, we construct a new component that is dedicated for the translation of difficult phrases. Our experiments on Arabic-to-English translation show that DTP-specific system customization is mostly successful.Overall, we demonstrate that translation difficulty is an important source of information for machine translation and can be used to enhance its performance

Multi-modal post-editing of machine translation

As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden Qualität maschineller Übersetzungssysteme (MÜ) post-editieren (PE) immer mehr Übersetzer die MÜ-Ausgabe, was im Vergleich zur herkömmlichen Übersetzung Zeit spart und Fehler reduziert. Anstatt primär Text zu generieren, müssen Übersetzer nun Fehler in ansonsten hilfreichen Übersetzungsvorschlägen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MÜ-Fehler mühsam und schwer zu erkennende Fehler fordern die Übersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere Interaktionsmodalitäten als Maus und Tastatur das PE unterstützen können, indem wir die Übersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder über Handgesten zu verändern, Wörter per Drag & Drop neu anzuordnen oder all diese Eingabemodalitäten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschätzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Übersetzungsumgebungen an die KL der Übersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen für das automatische Post-Editieren (APE) vor und erörtern, wie diese die MÜ-Ausgabe an eine Domäne anpassen und dadurch die Korrektur von sich wiederholenden MÜ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP