Multilingual Neural Translation

Machine translation (MT) refers to the technology that can automatically translate contents in one language into other languages. Being an important research area in the field of natural language processing, machine translation has typically been considered one of most challenging yet exciting problems. Thanks to research progress in the data-driven statistical machine translation (SMT), MT is recently capable of providing adequate translation services in many language directions and it has been widely deployed in various practical applications and scenarios. Nevertheless, there exist several drawbacks in the SMT framework. The major drawbacks of SMT lie in its dependency in separate components, its simple modeling approach, and the ignorance of global context in the translation process. Those inherent drawbacks prevent the over-tuned SMT models to gain any noticeable improvements over its horizon. Furthermore, SMT is unable to formulate a multilingual approach in which more than two languages are involved. The typical workaround is to develop multiple pair-wise SMT systems and connect them in a complex bundle to perform multilingual translation. Those limitations have called out for innovative approaches to address them effectively. On the other hand, it is noticeable how research on artificial neural networks has progressed rapidly since the beginning of the last decade, thanks to the improvement in computation, i.e faster hardware. Among other machine learning approaches, neural networks are known to be able to capture complex dependencies and learn latent representations. Naturally, it is tempting to apply neural networks in machine translation. First attempts revolve around replacing SMT sub-components by the neural counterparts. Later attempts are more revolutionary by fundamentally changing the whole core of SMT with neural networks, which is now popularly known as neural machine translation (NMT). NMT is an end-to-end system which directly estimate the translation model between the source and target sentences. Furthermore, it is later discovered to capture the inherent hierarchical structure of natural language. This is the key property of NMT that enables a new training paradigm and a less complex approach for multilingual machine translation using neural models. This thesis plays an important role in the evolutional course of machine translation by contributing to the transition of using neural components in SMT to the completely end-to-end NMT and most importantly being the first of the pioneers in building a neural multilingual translation system. First, we proposed an advanced neural-based component: the neural network discriminative word lexicon, which provides a global coverage for the source sentence during the translation process. We aim to alleviate the problems of phrase-based SMT models that are caused by the way how phrase-pair likelihoods are estimated. Such models are unable to gather information from beyond the phrase boundaries. In contrast, our discriminative word lexicon facilitates both the local and global contexts of the source sentences and models the translation using deep neural architectures. Our model has improved the translation quality greatly when being applied in different translation tasks. Moreover, our proposed model has motivated the development of end-to-end NMT architectures later, where both of the source and target sentences are represented with deep neural networks. The second and also the most significant contribution of this thesis is the idea of extending an NMT system to a multilingual neural translation framework without modifying its architecture. Based on the ability of deep neural networks to modeling complex relationships and structures, we utilize NMT to learn and share the cross-lingual information to benefit all translation directions. In order to achieve that purpose, we present two steps: first in incorporating language information into training corpora so that the NMT learns a common semantic space across languages and then force the NMT to translate into the desired target languages. The compelling aspect of the approach compared to other multilingual methods, however, lies in the fact that our multilingual extension is conducted in the preprocessing phase, thus, no change needs to be done inside the NMT architecture. Our proposed method, a universal approach for multilingual MT, enables a seamless coupling with any NMT architecture, thus makes the multilingual expansion to the NMT systems effortlessly. Our experiments and the studies from others have successfully employed our approach with numerous different NMT architectures and show the universality of the approach. Our multilingual neural machine translation accommodates cross-lingual information in a learned common semantic space to improve altogether every translation direction. It is then effectively applied and evaluated in various scenarios. We develop a multilingual translation system that relies on both source and target data to boost up the quality of a single translation direction. Another system could be deployed as a multilingual translation system that only requires being trained once using a multilingual corpus but is able to translate between many languages simultaneously and the delivered quality is more favorable than many translation systems trained separately. Such a system able to learn from large corpora of well-resourced languages, such as English → German or English → French, has proved to enhance other translation direction of low-resourced language pairs like English → Lithuania or German → Romanian. Even more, we show that kind of approach can be applied to the extreme case of zero-resourced translation where no parallel data is available for training without the need of pivot techniques. The research topics of this thesis are not limited to broadening application scopes of our multilingual approach but we also focus on improving its efficiency in practice. Our multilingual models have been further improved to adequately address the multilingual systems whose number of languages is large. The proposed strategies demonstrate that they are effective at achieving better performance in multi-way translation scenarios with greatly reduced training time. Beyond academic evaluations, we could deploy the multilingual ideas in the lecture-themed spontaneous speech translation service (Lecture Translator) at KIT. Interestingly, a derivative product of our systems, the multilingual word embedding corpus available in a dozen of languages, can serve as a useful resource for cross-lingual applications such as cross-lingual document classification, information retrieval, textual entailment or question answering. Detailed analysis shows excellent performance with regard to semantic similarity metrics when using the embeddings on standard cross-lingual classification tasks

UmobiTalk: Ubiquitous Mobile Speech Based Learning Language Translator for Sesotho Language

Published ThesisThe need to conserve the under-resourced languages is becoming more urgent as some of them are becoming extinct; natural language processing can be used to redress this. Currently, most initiatives around language processing technologies are focusing on western languages such as English and French, yet resources for such languages are already available. The Sesotho language is one of the under-resourced Bantu languages; it is mostly spoken in Free State province of South Africa and in Lesotho. Like other parts of South Africa, Free State has experienced high number of migrants and non-Sesotho speakers from neighboring provinces and countries; such people are faced with serious language barrier problems especially in the informal settlements where everyone tends to speak only Sesotho. Non-Sesotho speakers refers to the racial groups such as Xhosas, Zulus, Coloureds, Whites and more, in which Sesotho language is not their native language. As a solution to this, we developed a parallel corpus that has English as source and Sesotho as a target language and packaged it in UmobiTalk - Ubiquitous mobile speech based learning translator. UmobiTalk is a mobile-based tool for learning Sesotho for English speakers. The development of this tool was based on the combination of automatic speech recognition, machine translation and speech synthesis

462 Machine Translation Systems for Europe

We built 462 machine translation systems for all language pairs of the Acquis Communautaire corpus. We report and analyse the performance of these system, and compare them against pivot translation and a number of system combination methods (multi-pivot, multisource) that are possible due to the available systems.JRC.G.2-Global security and crisis managemen

Multilingual word embeddings and their utility in cross-lingual learning

Word embeddings - dense vector representations of a word’s distributional semantics - are an indespensable component of contemporary natural language processing (NLP). Bilingual embeddings, in particular, have attracted much attention in recent years, given their inherent applicability to cross-lingual NLP tasks, such as Part-of-speech tagging and dependency parsing. However, despite recent advancements in bilingual embedding mapping, very little research has been dedicated to aligning embeddings multilingually, where word embeddings for a variable amount of languages are oriented to a single vector space. Given a proper alignment, one potential use case for multilingual embeddings is cross-lingual transfer learning, where a machine learning model trained on resource-rich languages (e.g. Finnish and Estonian) can “transfer” its salient features to a related language for which annotated resources are scarce (e.g. North Sami). The effect of the quality of this alignment on downstream cross-lingual NLP tasks has also been left largely unexplored, however. With this in mind, our work is motivated by two goals. First, we aim to leverage existing supervised and unsupervised methods in bilingual embedding mapping towards inducing high quality multilingual embeddings. To this end, we propose three algorithms (one supervised, two unsupervised) and evaluate them against a completely supervised bilingual system and a commonly employed baseline approach. Second, we investigate the utility of multilingual embeddings in two common cross-lingual transfer learning scenarios: POS-tagging and dependency parsing. To do so, we train a joint POS-tagger/dependency parser on Universal Dependencies treebanks for a variety of Indo-European languages and evaluate it on other, closely related languages. Although we ultimately observe that, in most settings, multilingual word embeddings themselves do not induce a cross-lingual signal, our experimental framework and results offer many insights for future cross-lingual learning experiments

A computational approach to Zulu verb morphology within the context of lexical semantics

The central research question that is addressed in this article is: How can ZulMorph, a finite state morphological analyser for Zulu, be employed to add value to Zulu lexical semantics with specific reference to Zulu verbs? The verb is the most complex word category in Zulu. Due to the agglutinative nature of Zulu morphology, limited information can be computationally extracted from running Zulu text without the support of  sufficiently reliable computational mor-phological analysis by means of which the  essential meanings of, amongst others, verbs can be exposed. In this article we describe a corpus-based approach to adding the English meaning to Zulu extended verb roots, thereby enhancing ZulMorph as a lexical knowledge base.Keywords: Zulu Verb Morphology, Verb Extensions, Lexical Semantics, Computational Morphological Analysis, Zulmorph, Zulu Lexical Knowl-Edge Base, Bitext

Cross-lingual Dependency Parsing of Related Languages with Rich Morphosyntactic Tagsets

This paper addresses cross-lingual dependency parsing using rich morphosyntactic tagsets. In our case study, we experiment with three related Slavic languages: Croatian, Serbian and Slovene. Four different dependency treebanks are used for monolingual parsing, direct cross-lingual parsing, and a recently introduced crosslingual parsing approach that utilizes statistical machine translation and annotation projection. We argue for the benefits of using rich morphosyntactic tagsets in cross-lingual parsing and empirically support the claim by showing large improvements over an impoverished common feature representation in form of a reduced part-of-speech tagset. In the process, we improve over the previous state-of-the-art scores in dependency parsing for all three languages.Published versio