Show simple item record Parida, Shantipriya Bojar, Ondřej 2020-07-16T05:54:47Z 2020-07-16T05:54:47Z 2020-04-08
dc.description Data ----- We have collected English-Odia parallel data for the purposes of NLP research of the Odia language. The data for the parallel corpus was extracted from existing parallel corpora such as OdiEnCorp 1.0 and PMIndia, and books which contain both English and Odia text such as grammar and bilingual literature books. We also included parallel text from multiple public websites such as Odia Wikipedia, Odia digital library, and Odisha Government websites. The parallel corpus covers many domains: the Bible, other literature, Wiki data relating to many topics, Government policies, and general conversation. We have processed the raw data collected from the books, websites, performed sentence alignments (a mix of manual and automatic alignments) and released the corpus in a form suitable for various NLP tasks. Corpus Format ------------- OdiEnCorp 2.0 is stored in simple tab-delimited plain text files, each with three tab-delimited columns: - a coarse indication of the domain - the English sentence - the corresponding Odia sentence The corpus is shuffled at the level of sentence pairs. The coarse domains are: books ... prose text dict ... dictionaries and phrasebooks govt ... partially formal text odiencorp10 ... OdiEnCorp 1.0 (mix of domains) pmindia ... PMIndia (the original corpus) wikipedia ... sentences and phrases from Wikipedia Data Statistics --------------- The statistics of the current release are given below. Note that the statistics differ from those reported in the paper due to deduplication at the level of sentence pairs. The deduplication was performed within each of the dev set, test set and training set and taking the coarse domain indication into account. It is still possible that the same sentence pair appears more than once within the same set (dev/test/train) if it came from different domains, and it is also possible that a sentence pair appears in several sets (dev/test/train). Parallel Corpus Statistics -------------------------- Dev Dev Dev Test Test Test Train Train Train Sents # EN # OD Sents # EN # OD Sents # EN # OD books 3523 42011 36723 3895 52808 45383 3129 40461 35300 dict 3342 14580 13838 3437 14807 14110 5900 21591 20246 govt - - - - - - 761 15227 13132 odiencorp10 947 21905 19509 1259 28473 24350 26963 704114 602005 pmindia 3836 70282 61099 3836 68695 59876 30687 551657 486636 wikipedia 1896 9388 9385 1917 21381 20951 1930 7087 7122 Total 13544 158166 140554 14344 186164 164670 69370 1340137 1164441 "Sents" are the counts of the sentence pairs in the given set (dev/test/train) and domain (books/dict/...). "# EN" and "# OD" are approximate counts of words (simply space-delimited, without tokenization) in English and Odia The total number of sentence pairs (lines) is 13544+14344+69370=97258. Ignoring the set and domain and deduplicating again, this number drops to 94857. Citation -------- If you use this corpus, please cite the following paper: @inproceedings{parida2020odiencorp, title={OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation}, author={Parida, Shantipriya and Dash, Satya Ranjan and Bojar, Ond{\v{r}}ej and Motlicek, Petr and Pattnaik, Priyanka and Mallick, Debasish Kumar}, booktitle={Proceedings of the WILDRE5--5th Workshop on Indian Language Data: Resources and Evaluation}, pages={14--19}, year={2020} }
dc.language.iso ori
dc.language.iso eng
dc.publisher Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
dc.relation info:eu-repo/grantAgreement/EC/H2020/833635
dc.rights Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subject parallel corpus
dc.subject corpus
dc.subject machine translation
dc.subject under-resourced language
dc.title OdiEnCorp 2.0
dc.type corpus
metashare.ResourceInfo#ContentInfo.mediaType text
dc.rights.label PUB
has.files yes
contact.person Ondřej Bojar Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
sponsor European Union EC/H2020/833635 ROXANNE - Real time network, text, and speaker analytics for combating organized crime euFunds info:eu-repo/grantAgreement/EC/H2020/833635
sponsor InnoSuisse 29814.1 IP-ICT SM2: Extracting Semantic Meaning from Spoken Material” funding application no. 29814.1 IP-ICT Other
sponsor Grantová agentura České republiky 18-24210S Multilingual Machine Translation nationalFunds 97258 sentences
files.size 11000672
files.count 1

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