Exploiting Cross-Lingual Representations For Natural Language Processing

Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages. In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest

Character-level and syntax-level models for low-resource and multilingual natural language processing

There are more than 7000 languages in the world, but only a small portion of them benefit from Natural Language Processing resources and models. Although languages generally present different characteristics, “cross-lingual bridges” can be exploited, such as transliteration signals and word alignment links. Such information, together with the availability of multiparallel corpora and the urge to overcome language barriers, motivates us to build models that represent more of the world’s languages. This thesis investigates cross-lingual links for improving the processing of low-resource languages with language-agnostic models at the character and syntax level. Specifically, we propose to (i) use orthographic similarities and transliteration between Named Entities and rare words in different languages to improve the construction of Bilingual Word Embeddings (BWEs) and named entity resources, and (ii) exploit multiparallel corpora for projecting labels from high- to low-resource languages, thereby gaining access to weakly supervised processing methods for the latter. In the first publication, we describe our approach for improving the translation of rare words and named entities for the Bilingual Dictionary Induction (BDI) task, using orthography and transliteration information. In our second work, we tackle BDI by enriching BWEs with orthography embeddings and a number of other features, using our classification-based system to overcome script differences among languages. The third publication describes cheap cross-lingual signals that should be considered when building mapping approaches for BWEs since they are simple to extract, effective for bootstrapping the mapping of BWEs, and overcome the failure of unsupervised methods. The fourth paper shows our approach for extracting a named entity resource for 1340 languages, including very low-resource languages from all major areas of linguistic diversity. We exploit parallel corpus statistics and transliteration models and obtain improved performance over prior work. Lastly, the fifth work models annotation projection as a graph-based label propagation problem for the part of speech tagging task. Part of speech models trained on our labeled sets outperform prior work for low-resource languages like Bambara (an African language spoken in Mali), Erzya (a Uralic language spoken in Russia’s Republic of Mordovia), Manx (the Celtic language of the Isle of Man), and Yoruba (a Niger-Congo language spoken in Nigeria and surrounding countries)

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

Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

Development of the multilingual semantic annotation system

This paper reports on our research to generate multilingual semantic lexical resources and develop multilingual semantic annotation software, which assigns each word in running text to a semantic category based on a lexical semantic classification scheme. Such tools have an important role in developing intelligent multilingual NLP, text mining and ICT systems. In this work, we aim to extend an existing English semantic annotation tool to cover a range of languages, namely Italian, Chinese and Brazilian Portuguese, by bootstrapping new semantic lexical resources via automatically translating existing English semantic lexicons into these languages. We used a set of bilingual dictionaries and word lists for this purpose. In our experiment, with minor manual improvement of the automatically generated semantic lexicons, the prototype tools based on the new lexicons achieved an average lexical coverage of 79.86% and an average annotation precision of 71.42% (if only precise annotations are considered) or 84.64% (if partially correct annotations are included) on the three languages. Our experiment demonstrates that it is feasible to rapidly develop prototype semantic annotation tools for new languages by automatically bootstrapping new semantic lexicons based on existing ones

Extending a multilingual lexical resource by bootstrapping named entity classification using Wikipedia's category system

Named Entity Recognition and Classification (NERC) is a well-studied NLP task which is typically approached using machine learning algorithms that rely on training data whose creation usually is expensive. The high costs result in the lack of NERC training data for many languages. An approach to create a multilingual NE corpus was presented in Wentland et al. (2008). The resulting resource called HeiNER describes a valuable number of NEs but does not include their types. We present a bootstrap approach based on Wikipedia’s category system to classify the NEs contained in HeiNER that is able to classify more than two million named entities to improve the resource’s quality

Multi-feature Based Chinese-English Named Entity Extraction from Comparable Corpora

PACLIC 20 / Wuhan, China / 1-3 November, 200