Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing

By Matt Post, Chris Callison-Burch, Miles Osborne. 2012

146 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, methodology): “We made use of the Urdu-English segment of the Indic multi-parallel corpus (Post et al., 2012) which contains roughly 87K sentence pairs.”
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages (2020, background): “MT evaluations by humans are often done either within a group of researchers from the same lab or ﬁeld (e.g. for WMT evaluations 14 ), or via crowdsourcing platforms (Ambati and Vogel, 2010; Post et al., 2012).”
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English (2019, background): “In the past, there has been work on extracting high quality translations from crowd-sourced workers using automatic methods (Zaidan and Callison-Burch, 2011; Post et al., 2012).”
Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English (2019, background): “In the past, there has been work on extracting high quality translations from crowd-sourcing using automatic methods (Zaidan and Callison-Burch, 2011), and cleaning them through voting mechanisms (Post et al., 2012).”
Integrating an Unsupervised Transliteration Model into Statistical Machine Translation (2014, methodology): “For Indian languages we used the Indic multi-parallel corpus (Post et al., 2012), and we used the dev and test sets provided with the parallel corpus.”
The Language Demographics of Amazon Mechanical Turk (2014, methodology): “Training SMT systems We trained statistical translation models from the parallel corpora that we created for the six Indian languages using the Joshua machine translation system (Post et al., 2012).”
A Survey of Multilingual Neural Machine Translation (2019, background): “In addition, there are other small parallel datasets like the FLORES dataset for English-{Nepali,Sinhala} [57], the XNLI test set spanning 15 languages [34], CVIT-Mann ki Baat [136] and the Indic parallel corpus [116].”
Improving machine translation via triangulation and transliteration (2014, unknown): “The model for Urdu-English data was build us-ing Urdu-English segment of the Indic 5 multi-parallel corpus (Post et al., 2012) which contain roughly 87K sentences.”
HindEnCorp - Hindi-English and Hindi-only Corpus for Machine Translation (2014, unknown): “For the current release, we are extending the parallel corpus using these sources: The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical…”
Findings of the WMT 2016 Bilingual Document Alignment Shared Task (2016, background): “To give an example, there are significant research efforts on various Indic languages (Post et al., 2012; Joshi et al., 2013; Singh, 2013), but this work has been severely hampered, since it uses very small amounts of data.”