Ensuring Inclusion and Diversity in Research and Research Output: A Case for a Language-Sensitive NLP Crowdsourcing Platform

Abstract

In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant and applicable across issues and domains. This paper also makes the case that by enabling participation for a wide variety of stakeholders, this NLP crowdsourcing platform might ultimately prove useful in the decision- and policy-making processes at city, community, and country levels. Against the backdrop of the debates on artificial intelligence (AI) and NLP research, and considering substantial differentiation specific to the Arab language, this paper introduces and evaluates an Arab language-sensitive NLP crowdsourcing platform. The value of the platform and its accuracy are measured via the System Usability Scale (SUS), where it scores 72.5, i.e., above the accepted usability average. These findings are crucial for NLP research and the research community in general. They are equally promising in view of the practical application of the research findings.

Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models

“…While transfer learning techniques (Ruder et al, 2019;Rahimi et al, 2019;Conneau et al, 2020) shine a ray of hope for increasing language diversity, Joshi et al (2020) challenge the optimism towards transfer learning for multilingual NLP by highlighting that many low-resource languages contain typological features not adequately represented in richer resource languages like English. Other efforts revolve around data collection (Alahmadi et al, 2020;Koto et al, 2020;Augustyniak et al, 2022;Marreddy et al, 2022), which improve data collection techniques for low-resource languages and facilitate the participation of indigenous people in data collection for NLP research.…”

The Gig Economy: Current Issues, the Debate, and the New Avenues of Research

“…Uber offers physical local work for low-skilled workers. Fully virtual and global work is offered by MTurk (Amazon Mechanical Turk, a crowdsourcing website) for low-skilled workers, but examples of gig economy platforms for highly skilled professionals, such as LabMate, were also analyzed [11].…”

“…One of the corollaries of the emergence of platform economy is the phenomenon of gig economy. In contrast to the traditional definition of gig economy that used to denote short-term contracts and freelance work, as opposed to permanent jobs, the onset of platform economy redefines the very concept of the gig economy [9][10][11]. In other words, the ever more advanced technological solutions, involving blockchain, smart contract, and AI, enhance the capacities, opportunities, and economic synergies that the, by now digital, platforms can deliver.…”

A Crowdsourcing-Based Framework for the Development and Validation of Machine Readable Parallel Corpus for Sign Languages

“…Researchers have discussed the use of crowdsourcing for different types of natural language processing tasks [28,46]. In particular, a high-level approach for building a crowdsourcing-based sign language dictionary has been discussed in [29] and [30].…”

“…Similarly, special purpose and language-specific crowdsourcing platforms have also been proposed e.g. [46] proposed a crowdsourcing platform for collecting and annotating Arabic language tweets.…”