Challenges of Building Domain-Specific Parallel Corpora from Public Administration Documents
PRINCIPLE was a Connecting Europe Facility (CEF)-funded project that focused on the identification, collection and processing of language resources (LRs) for four European under-resourced languages (Croatian, Icelandic, Irish and Norwegian) in order to improve translation quality of eTranslation, an online machine translation (MT) tool provided by the European Commission. The collected LRs were used for the development of neural MT engines in order to verify the quality of the resources. For all four languages, a total of 66 LRs were collected and made available on the ELRC-SHARE repository under various licenses. For Croatian, we have collected and published 20 LRs: 19 parallel corpora and 1 glossary. The majority of data is in the general domain (72 % of translation units), while the rest is in the eJustice (23 %), eHealth (3 %) and eProcurement (2 %) Digital Service Infrastructures (DSI) domains. The majority of the resources were for the Croatian-English language pair. The data was donated by six data contributors from the public as well as private sector. In this paper we present a subset of 13 Croatian LRs developed based on public administration documents, which are all made freely available, as well as challenges associated with the data collection, cleaning and processing.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Building Machine Translation System for Software Product Descriptions Using Domain-specific Sub-corpora Extraction
Building Machine Translation systems for a specific domain requires a sufficiently large and good quality parallel corpus in that domain. However, this is a bit challenging task due to the lack of parallel data in many d…
Machine TranslationSentenceSentence EmbeddingSentence-Embedding+1The AMARA Corpus: Building Parallel Language Resources for the Educational Domain
This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. Th…
Machine TranslationTranslationBuilding Comparable Corpora for Assessing Multi-Word Term Alignment
Recent work has demonstrated the importance of dealing with Multi-Word Terms (MWTs) in several Natural Language Processing applications. In particular, MWTs pose serious challenges for alignment and machine translation s…
Machine TranslationHarvesting comparable corpora and mining them for equivalent bilingual sentences using statistical classification and analogy- based heuristics
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our new methodologies for minin…
General ClassificationMachine TranslationRetrievalTranslationBuilding and Modelling Multilingual Subjective Corpora
Building multilingual opinionated models requires multilingual corpora annotated with opinion labels. Unfortunately, such kind of corpora are rare. We consider opinions in this work as subjective or objective. In this pa…
Language ModellingMachine TranslationOpinion MiningSentiment Analysis+1