Neural machine translation for translating into Croatian and Serbian

In this work, we systematically investigate different set-ups for training of neural machine translation (NMT) systems for translation into Croatian and Serbian, two closely related South Slavic languages. We explore English and German as source languages, different sizes and types of training corpora, as well as bilingual and multilingual systems. We also explore translation of English IMDb user movie reviews, a domain/genre where only monolingual data are available. First, our results confirm that multilingual systems with joint target languages perform better. Furthermore, translation performance from English is much better than from German, partly because German is morphologically more complex and partly because the corpus consists mostly of parallel human translations instead of original text and its human translation. The translation from German should be further investigated systematically. For translating user reviews, creating synthetic in-domain parallel data through back- and forward-translation and adding them to a small out-of-domain parallel corpus can yield performance comparable with a system trained on a full out-of-domain corpus. However, it is still not clear what is the optimal size of synthetic in-domain data, especially for forward-translated data where the target language is machine translated. More detailed research including manual evaluation and analysis is needed in this direction

Primerjava običajnih in faktorskih modelov pri statističnem strojnem prevajanju iz angleščine v slovenščino z orodjem Moses

Strojno prevajanje je področje računalniške lingvistike, ki raziskuje uporabo programske opreme za prevajanje besedila iz enega jezika v drugega. Faktorsko statistično strojno prevajanje je različica statističnega, pri katerem besedilu dodamo jezikoslovne oznake na ravni besed in jih spremenimo v vektorje. Tako želimo izboljšati kakovost dobljenih prevodov. V prispevku opišemo uporabo odprtokodnega sistema Moses za faktorsko statistično strojno prevajanje iz angleščine v slovenščino. Iz besedilnega korpusa smo ustvarili več faktorskih in nefaktorskih prevajalnih modelov. Z njimi smo prevedli dve besedili s področja informacijskih tehnologij. Prvo je usmerjeno tržno in ima kompleksnejšo zgradbo, drugo pa je bolj tehnične narave. Prevode, ki smo jih dobili, smo na dva načina primerjali z dvema neodvisnima človeškima prevodoma in s prevodom, ki smo ga ustvarili s storitvijo Google Translate. Za prvi način primerjave smo uporabili metriko BLEU, za drugega pa so prevode pregledali človeški pregledovalci in podali subjektivno oceno, ki je pri prevajanju še vedno zelo pomembna. Čeprav rezultatov ne moremo primerjati neposredno zaradi različnih metrik, se gibanje ocen kakovosti pri obeh besedilih dobro ujema. Edina občutna razlika med računalniško in človeško oceno se pojavi pri prehodu na faktorske modele pri drugem besedilu. Analizirali smo zanesljivost ocenjevalcev in rezultate ocenjevanja. Ugotovili smo, da so naši modeli primernejši za tehnična besedila in da uporaba faktorskih modelov vidneje izboljša prevajanje kompleksnejših besedil

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

CLARIN. The infrastructure for language resources

CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future. The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium