Parallel Corpora as Digital Resources and Their Applications

Olga Lyashevskaya (National Research University Higher School of Economics, V. V. Vinogradov Russian Language Institute RAS, Moscow)

Belarusian-Russian and Lithuanian-Russian parallel treebanks: three practical tasks, three dozen dependency relations, and an indefinite number of language-specific constructions

We present two pilot parallel treebanks for related, East Slavic and Baltic, languages annotated according to the Universal Dependencies (UD) scheme (Nivre et al. 2017). Belarusian-Russian and Lithuanian-Russian treebanks are annotated at the level of full morphology and dependencies, lemmatized, and aligned at the sentence-to sentence and word-to-word level. All annotations are checked manually. The development of our resources is based on the experience of several parallel dependency projects, such as Prague Czech-English dependency treebank (Hajič et al. 2012 , Urešová et al. 2019), LinES English-Swedish treebank (Ahrenberg 2007), ParTUT, a corpus for Italian, English and French (Bosco et al. 2012), and PROIEL, a parallel treebank of the old Indo-European Bible translations (Haug, Jøhndal 2008). Some of these treebanks were converted to UD (Ahrenberg 2015; Ekhoff et al. 2018), and a multilingual Parallel UD collection was introduced in the CONLL 2017 shared task (Zeman et al. 2017).

Alignment of texts written in closely related languages is often considered a trivial task. However, the non-matching parts of trees allows one to reveal language-specific constructions (e.g. Be. больш як 15 працэнтаў урачоў - Ru. более 15 процентов врачей 'more than 15 per cent doctors') or tendencies to different choice between rival patterns and word orders in two languages. On the one hand, the systematic analysis of such mismatches paves the way towards quantitative token-based typology (Levshina 2019, 2015; Haspelmath et al. 2014; Bjerva et al. 2019; Guzmán Naranjo et al. 2018) and identifies dimensions for developing the multi-lingual constructicons and FrameNets (Lyngfeldt et al. 2018; Boas, Höder 2018; Gilardi, Baker 2018). On the other hand, construction alignment can be useful to ensure the quality of corpus annotation (Ahrenberg 2019).

Taking such a practical perspective, we propose an approach based on parallel annotations to define language-specific UD shemas and to assist annotators who don't possess extensive expertise in a divergent, although related to his/her native, language. Moreover, the parallel tree alignment is helpful while evaluating the quality of texts (e. g. news, wikipedia articles) selected for including into the parallel collections.


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