The emergence of crowdsourcing as a force to power a language revolution

What is crowdsourcing?

Crowdsourcing is commonly referred to as a process that enables organizations or individuals to obtain content, services, or even ideas by requesting contributions from a large group of people, usually through an online community or a digital platform where people participate as volunteers for projects that produce a common good, like the digital encyclopedia *Wikipedia. In many cases, these crowdsourced workers are paid for the work they provide for-profit organizations like Upwork. The term “crowdsourcing” was first coined in 2006 by Jeff Howe and Mark Robinson in an article for the magazine [Wired](https://www.wired.com/2006/06/crowds/)* that described how companies used the crowd’s power to outsource work, not to a remote location, but to the Internet.

Crowdsourcing is turning to a group of people to obtain information, knowledge, goods, or services.

Crowdsourcing combines the words “crowd” and “outsourcing.” However, in contrast to outsourcing, where people are hired in specific places to perform particular jobs, crowdsourcing typically incorporates broader, less specific participant groups for general tasks (e.g., a data collection project that requires non-native English speakers regardless of their background or geographical location). Often, the only thing these groups have in common is that they make themselves relevant, visible, and available through the Internet. However, it can also rely on particular groups for more specialized projects (e.g., the same data collection project, but, this time, only dependent on non-native English speakers born and raised in Bali, Indonesia).

First, the Internet; then, your online persona

Crowdsourcing at this age is made possible by people who make themselves available online.

The Internet has become an integral part of daily life, providing access to information and resources that can be invaluable. However, not everyone has access to the Internet, which can limit their ability to take advantage of its many benefits. Universal access to the Internet can potentially level the playing field, providing everyone with the same opportunities to learn, connect, grow, contribute and earn. It can help bridge the digital divide, connecting those otherwise isolated from the vast wealth of online knowledge and resources. There are many reasons why universal access to the Internet is essential, and we should strive to achieve it. For one, it can promote education and a lifelong learning path that can help people learn new things and improve their skills. The Internet can be an excellent tool for communication and connection that can help people connect with others who share their interests and experiences. Finally, the Internet can be a great source of economic opportunity for people who would otherwise have difficulty making a living based on their location.

Languages and the Internet

Crowdsourcing as a source of extensive information.

The Internet has been a boon for language learners. Identifying resources to help you learn a new language, practice your skills, or find a community of like-minded learners has never been more accessible. But one of the most exciting developments in recent years has been the emergence of crowdsourcing to boost language learning. With the help of the Internet, language learners around the world are coming together to create and curate language learning resources like never before. From online dictionaries and forums to quizzes and podcasts, a wealth of language-learning material is available at the click of a button, with much of it created by passionate language learners. This crowdsourced approach to language learning has democratized the process, making it possible for individuals with an Internet connection to access high-quality language learning material. It has also given rise to a new generation of language learners who are not afraid to experiment and innovate to become fluent in a new language and create new opportunities in the process.

The real language revolution

A revolution powered by people’s words.

The language revolution doesn’t stop with language acquisition. In recent years, there has been a rise in the use of crowdsourcing to collect language data for both text and voice, given its cost-effectiveness in gathering specific data from many people. Moreover, it allows for collecting data from various sources, which can be helpful in research projects. Crowdsourcing has been used to collect data on multiple topics, including the use of specific words, the meaning of words, and the grammar of a language. This data can be used to create dictionaries, study a language’s evolution, develop language learning resources, or build even more complex applications like language recognition devices and voice assistants similar to Siri or Alexa. Now imagine if these AI assistants were available in any language. This process will, no doubt, begin with a group of crowdsourced contributors who will bring their talent and knowledge to make it possible, from the crowd and for the crowd. AI-based applications in any language are now more feasible than ever, and crowdsourcing is clearly the power behind this language revolution.

Advantages and disadvantages of language data crowdsourcing

Crowdsourcing involves a large group of dispersed participants of all ages, ethnicities, etc.

Crowdsourcing has become a popular way for businesses to rapidly generate a large amount of language data content cost-effectively as it makes data collection from a large variety of people easy, allowing the data to represent a larger population. It also allows for the data to be collected from various geographical locations, which is helpful for text data, but even more so for voice data, given the different pronunciations and intonations that regional variants of a language tend to have. From the worker’s perspective, some of the benefits of working as a crowdsourced worker include working from anywhere, choosing your own hours, and performing various tasks from the comfort of your home.

Although crowdsourcing’s advantages are evident, it also has its disadvantages. Because the data collected through crowdsourcing is from various sources, some of the data may be unreliable and low in quality. Moreover, some data may be biased as people may be more likely to provide data that is favorable to their point of view. Hiring a reliable roster of crowdsourced workers is also a challenge, together with keeping the quality of their output in check. If you do not have well-thought-out onboarding, training, and evaluation strategies, then crowdsourcing on your own would be impossible.

In natural language processing, crowdsourcing can be challenging, given the vast amount of text or voice data that needs to be processed and labeled. Currently, there is no one way to control quality in large crowdsourcing projects. Some companies may have a system that rates the quality of submissions from freelancers and only allows top-rated freelancers to continue working on the project. Others may use a system of peer review, where submissions are reviewed and rated by other freelancers working on the same project. Still, others may simply rely on the client to rate submissions’ quality and provide freelancers feedback. Despite all these disadvantages, crowdsourcing is a valuable resource for collecting language data that will keep rising in the coming years.

Crowdsourcing and data annotation

Crowdsourcing for data advancement.

Crowdsourcing is often used to create or improve training data for natural language processing models. For example, a model trained to identify named entities in a text might be trained using a dataset that human annotators have labeled. Language data labeling is the process of assigning labels to data so that it can be classified according to language, topic, usage, and other factors. This is usually done by humans, who look at the data and decide which labels apply to the inspected data. With that, there are a few different ways in which language data annotation can work. For example, the annotator can read through the text and mark it according to predefined rules. The annotator can also use a tool that will automatically annotate the text according to some set of rules.

Crowdsourcing for translation

Crowdsourcing allows the creation of a large base of translators with a wide variety of native tongues.

As a popular way to get things done quickly and efficiently, translation can also rely on crowdsourcing to tackle larger projects. This process typically applies some form of AI-powered translation that is then human edited. While some may be hesitant to trust the work to a crowd, there are many advantages to using crowdsourcing for translation. For one, it can be a cost-effective solution, especially when working with a limited budget. Using a crowd can also be faster than working with a single translator, as you can have multiple people working on the project simultaneously. Crowdsourcing can also be an excellent way to get a variety of perspectives on the text you are translating. This can be especially helpful if you work with a text open to interpretation. By getting multiple translations, you can better understand the text’s meaning and how it should be interpreted. Of course, it also comes with its limitations, such as how the quality of the work done without supervision from the essential team can be variable as you rely on the work of many different people. It is essential to carefully vet the crowd you are working with to ensure they are capable of delivering high-quality work, and to have clear guidelines for approaching the translation and the terms that should be used. Working with a limited group of individual professionals is the preferred method if you are working on a sensitive or confidential project. Overall, crowdsourcing can be an excellent solution for translation projects as long as you recognize and prepare for the potential risks and disadvantages.

Crowdsourcing platforms

BAVL’s bavl.ai platform ready to take on your data projects.

Crowdsourcing for language data and related tasks can work in various ways. One common method is to use a platform like Amazon Mechanical Turk to post jobs that require human judgment or expertise, such as identifying the language of a given text. Other platforms like LinguaList can be used to post language-related tasks and find language experts to complete them. BAVL, our proprietary platform, offers numerous specialized services to clients working on natural language processing projects that require large datasets. To accomplish this, BAVL also provides qualified AI workers, wherever they might be around the world, the chance to join exciting projects they can work on from the comfort of their homes — from data generation in any language to data annotations and from voice recording to data translation into multiple languages and everything in between.

Consider this a call to action. If you want to join as a contributor and become a BAVLer, visit bavl.ai now and click on the Join Us button to create a BAVL account and learn more about all the opportunities we offer.