This article presents an application of a multilingual and multidirectional parallel corpus composed of literary texts in five Romance languages (Spanish, French, Italian, Portuguese, Romanian) and a Slavic language (Croatian), with a total of 142,000 segments and 15.7 million words. After combining it with very large freely available parallel corpora, this resource is used to train NMT systems tailored to literature. A total of five NMT systems have been trained: Spanish-French, Spanish-Italian, Spanish-Portuguese, Spanish-Romanian and Spanish-Croatian. The trained systems were evaluated using automatic metrics (BLEU, chrF2 and TER) and a comparison with a rule-based MT system (Apertium) and a neural system (Google Translate) is presented. As a main conclusion, we can highlight that the use of this literary corpus has been very productive, as the majority of the trained systems achieve comparable, and in some cases even better, values of the automatic quality metrics than a widely used commercial NMT system.

This paper describes the building of the first Spanish-Croatian unidirectional parallel corpus, which has been constructed at the Faculty of Humanities and Social Sciences of the University of Zagreb. The corpus is comprised of eleven Spanish novels and their translations to Croatian done by six different professional translators. All the texts were published between 1999 and 2012. The corpus has more than 2 Mw, with approximately 1 Mw for each language. It was automatically sentence segmented and aligned, as well as manually post-corrected, and contains 71,778 translation units. In order to protect the copyright and to make the corpus available under permissive CC-BY licence, the aligned translation units are shuffled. This limits the usability of the corpus for research of language units at sentence and lower language levels only. There are two versions of the corpus in TMX format that will be available for download through META-SHARE and CLARIN ERIC infrastructure. The former contains plain TMX, while the latter is lemmatised and POS-tagged and stored in the aTMX format.

In this paper, a tool specifically designed to allow for complex searches in large parallel corpora is presented. The formalism for the queries is very powerful as it uses standard regular expressions that allow for complex queries combining word forms, lemmata and POS-tags. As queries are performed over POS-tags, at least one of the languages in the parallel corpus should be POS-tagged. Searches can be performed in one of the languages or in both languages at the same time. The program is able to POS-tag the corpora using the Freeling analyzer through its Python API. ReSiPC is developed in Python version 3 and it is distributed under a free license (GNU GPL). The tool can be used to provide data for contrastive linguistics research and an example of use in a Spanish-Croatian parallel corpus is presented. ReSiPC is designed for queries in POS-tagged corpora, but it can be easily adapted for querying corpora containing other kinds of information.