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4 results for “Makerere”
The Makerere Gendered Corpus: A Gendered English to Luganda Parallel Corpus
<p>This English-Luganda parallel sentence corpus consists of gendered examples created by a team of researchers from Makerere AI Lab at Makerere University with a team of Luganda teachers, students and freelancers. The collaborative work which involves generating English sentences under CC-0 and translating these sentences using a crowdsourcing, iterative and opensource approach was done using Pontoon an opensource Translation Management System built by Mozilla. This is a corpus of 1,000 parallel sentences.</p>
The Makerere MT Corpus: English to Luganda parallel corpus
<p>This English-Luganda parallel sentence corpus was created by a team of researchers from AI & Data science research Lab at Makerere University with a team of Luganda teachers, students and freelancers. The collaborative work which involves generating English sentences under CC-0 and translating these sentences using a crowdsourcing, iterative and opensource approach was done using Pontoon an opensource Translation Management System built by Mozilla.</p> <p>Acknowledgment: This project was supported by the <a href="https://www.k4all.org/project/language-dataset-fellowship/">AI4D language dataset fellowship</a> through K4All and <a href="https://zindi.africa/">Zindi Africa</a>.</p>
Data for article: Normative drift and self-correction and self-regulation in scholarly publishing: The case of scholarly books published by academics at Makerere University
<p>University academics face multiple pressures to publish. These pressures emanate from contexts with different, often competing, social norms which result in academics publishing for reasons that may run counter to accepted scientific practice. This paper asks what decisions are being taken by academics when it comes to their choice of scholarly book publisher. An analysis of books selected from more than 2,500 self-reported scholarly publications produced by academics at Makerere University in Uganda from 2011 to 2017, reveals that 31 scholarly books were published. Of these books, more than half (54%) were published by publishers that do not follow accepted scholarly publishing practice. Findings also show that there was a sharp decline in books published with suspect publishers in the second half of the seven-year period. The article discusses possible reasons for the selection of suspect publishers and considers four factors that may account for the observable decline: (1) a cyclical downturn; (2) improved research management and reporting; (3) explication of the norms of science; and (4) self-correction.</p> <p><strong>METHODS</strong></p> <p>The specific interest of this study is the publication of scholarly books by academics at Makerere University. </p> <p>The sources of the publications data were the lists of research outputs published in Makerere University’s annual reports from 2011 to 2017. Digital copies of the annual reports in PDF format were downloaded from the university’s website. Data were scraped from the annual report PDFs by exporting the publication lists from the PDFs to MSWord, removing unnecessary line breaks and spacing to create a single publication per line, and copying the publication lists into MSExcel for cleaning and analysis. Data were cleaned to remove (1) duplicate entries (e.g. identical publications in the same annual report or a publication listed as ‘under review’ or ‘in press’ in one year and listed again as a publication in a following year); (2) publications listed as ‘submitted’, unpublished masters and PhD theses, speeches, lectures, responses or retractions; (3) incomplete entries (e.g. no article, book or journal title); and (4) publications prior to 2011 (e.g. a 2008 publication listed in the 2011 annual report).</p> <p>The cleaned data were then coded for ‘year of publication’ (2011 to 2017 inclusive), ‘college’ (any of the colleges at Makerere University to which the authors were affiliated), and ‘publication type’ (book chapter, journal article, edited volume, working paper, policy brief, monograph, conference paper, textbook, report, popular article, poster presentation, newsletter, essay, catalogue, poetry, handbook). Books or parts of books (i.e. ‘book chapter’, ‘edited volume’, ‘monograph’, ‘textbook’, ‘catalogue’, ‘poetry’ and ‘handbook’) were further coded for ‘publisher’ (name); ‘publisher location’ (city, country as indicated on the imprint page of the publication); and ‘URL’ (online reference to publisher or publication). In some cases, publications were listed by type in the annual reports. Where publication lists were not presented in this way, indicators such as ‘journal of’, ‘volume’, ‘issue’ and ISSNs were used to identify journals; conference names, ‘proceedings of’, dates and cities were used to identify conference papers and presentations; ‘news’, ‘magazine’, ISSNs and the absence of ‘journal of’ were used to identify periodicals; and place of publication, publisher and ISBNs were used to identify books. Where these indicators were insufficient to determine a publication type, an online search was done to find the missing data. </p> <p>A limitation of the adopted method is that it relies on self-reporting by the university colleges and the list can therefore not be assumed to be comprehensive. Without knowledge of the reporting guidelines, it is also not possible to know whether all colleges and schools used the same criteria when selecting what to submit for reporting purposes. For example, some colleges may have decided to include working papers, policy briefs or papers presented at conferences, while others may have decided not to do so. </p>
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition
<p>The Makerere AI Lab has built an end-to-end CTC Luganda ASR model using radio data. Having encountered data challenges in working with low resource languages, we take the initiative together with our partners to release the first radio corpus for Luganda.</p> <p>The corpus of 155 hours is publicly available online under the Creative Commons BY-NC-ND 4.0 license. The dataset release is comprised of the following:</p> <ol> <li>20 hours of human transcribed radio speech. The audio is 16kHZ, mono channel and with 16 bit rate. </li> <li>Two CSV files for the 20-hour human transcribed dataset - cleaned.csv contains cleaned transcripts and uncleaned.csv contains uncleaned transcripts. The uncleaned transcripts contain extra speech details included in tags like [laughter] for laughter, and [um] for filler pauses, which speaker is talking, where each speaker is assigned an identifier A or B.</li> <li>The transcription guide used to transcribe the radio dataset.</li> <li> A multi-speaker untranscribed dataset of 6 hours of radio data. 1.4 hours of women voices and 4.6 hours of men voices. Each audio is a ten-seconds clip with a single speaker.</li> <li> 135 hours of multi-speaker untranscribed radio data.</li> </ol> <p><strong>NOTE: You can read and cite our paper published in the </strong><a href="http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.208.pdf"><strong>Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)</strong></a> The Dataset is published under Creative Commons BY-NC-ND 4.0 license and in order for us to monitor who is using it for the right license we request that you reach out to us officially. </p>
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