This paper provides an overview and update on the Linguistic Data Consortium’s (LDC) NIEUW (Novel Incentives and Workflows) project supported by the National Science Foundation and part of LDC’s larger goal of improving the cost, variety, scale, and quality of language resources available for education, research, and technology development. NIEUW leverages the power of novel incentives to elicit linguistic data and annotations from a wide variety of contributors including citizen scientists, game players, and language students and professionals. In order to align appropriate incentives with the various contributors, LDC has created three distinct web portals to bring together researchers and other language professionals with participants best suited to their project needs. These portals include LanguageARC designed for citizen scientists, Machina Pro Linguistica designed for students and language professionals, and LingoBoingo designed for game players. The design, interface, and underlying tools for each web portal were developed to appeal to the different incentives and motivations of their respective target audiences.

There is a growing interest in the evaluation of bias, fairness and social impact of Natural Language Processing models and tools. However, little resources are available for this task in languages other than English. Translation of resources originally developed for English is a promising research direction. However, there is also a need for complementing translated resources by newly sourced resources in the original languages and social contexts studied. In order to collect a language resource for the study of biases in Language Models for French, we decided to resort to citizen science. We created three tasks on the LanguageARC citizen science platform to assist with the translation of an existing resource from English into French as well as the collection of complementary resources in native French. We successfully collected data for all three tasks from a total of 102 volunteer participants. Participants from different parts of the world contributed and we noted that although calls sent to mailing lists had a positive impact on participation, some participants pointed barriers to contributions due to the collection platform.

In this study, we present the Fearless Steps APOLLO Community Resource, a collection of audio and corresponding meta-data diarized from the NASA Apollo Missions. Massive naturalistic speech data which is time-synchronized, without any human subject privacy constraints is very rare and difficult to organize, collect, and deploy. The Apollo Missions Audio is the largest collection of multi-speaker multi-channel data, where over 600 personnel are communicating over multiple missions to achieve strategic space exploration goals. A total of 12 manned missions over a six-year period produced extensive 30-track 1-inch analog tapes containing over 150,000 hours of audio. This presents the wider research community a unique opportunity to extract multi-modal knowledge in speech science, team cohesion and group dynamics, and historical archive preservation. We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science. Here we present the development of this community resource, our outreach efforts, and technological developments resulting from this data. We finally discuss the planned future directions for this community resource.

This work presents the path toward the creation of eight Spoken Language Resources under the umbrella of the Mexican Social Service national program. This program asks undergraduate students to donate time and work for the benefit of their society as a requirement to receive their degree. The program has thousands of options for the students who enroll. We show how we created a program which has resulted in the creation of open language resources which now are freely available in different repositories. We estimate that this exercise is equivalent to a budget of more than half a million US dollars. However, since the program is based on retribution from the students to their communities there has not been a necessity of a financial budget.

In this paper, we present a novel approach to data collection for natural language processing (NLP), linguistic research and lexicographic work. Using the parlor game Fictionary as a framework, data can be crowd-sourced in a gamified manner, which carries the potential of faster, cheaper and better data when compared to traditional methods due to the engaging and competitive nature of the game. To improve data quality, the game includes a built-in review process where players review each other’s data and evaluate its quality. The paper proposes several games that can be used within this framework, and explains the value of the data generated by their use. These proposals include games that collect named entities along with their corresponding type tags, question-answer pairs, translation pairs and neologism, to name only a few. We are currently working on a digital platform that will host these games in Icelandic but wish to open the discussion around this topic and encourage other researchers to explore their own versions of the proposed games, all of which are language-independent.

This paper describes our use of mixed incentives and the citizen science portal LanguageARC to prepare, collect and quality control a large corpus of object namings for the purpose of providing speech data to document the under-represented Guanzhong dialect of Chinese spoken in the Shaanxi province in the environs of Xi’an.

Five participants, each located in distinct locations (USA, Canada, South Africa, Scotland and (South East) England), identified the self-determined social class of a corpus of 227 speakers (born 1986–2001; from South East England) based on 10-second passage readings. This pilot study demonstrates the potential for using crowdsourcing to collect sociolinguistic data, specifically using LanguageARC, especially when geographic spread of participants is desirable but not easily possible using traditional fieldwork methods. Results show that, firstly, accuracy at identifying social class is relatively low when compared to other factors, including when the same speech stimuli were used (e.g., ethnicity: Cole 2020). Secondly, participants identified speakers’ social class significantly better than chance for a three-class distinction (working, middle, upper) but not for a six-class distinction. Thirdly, despite some differences in performance, the participant located in South East England did not perform significantly better than other participants, suggesting that the participant’s presumed greater familiarity with sociolinguistic variation in the region may not have been advantageous. Finally, there is a distinction to be made between participants’ ability to pinpoint a speaker’s exact social class membership and their ability to identify the speaker’s relative class position. This paper discusses the role of social identification tasks in illuminating how speech is categorised and interpreted.

In this article, we present a recent trend of approaches, hereafter referred to as Collect4NLP, and discuss its applicability. Collect4NLP-based approaches collect inputs from language learners through learning exercises and aggregate the collected data to derive linguistic knowledge of expert quality. The primary purpose of these approaches is to improve NLP resources, however sincere concern with the needs of learners is crucial for making Collect4NLP work. We discuss the applicability of Collect4NLP approaches in relation to two perspectives. On the one hand, we compare Collect4NLP approaches to the two crowdsourcing trends currently most prevalent in NLP, namely Crowdsourcing Platforms (CPs) and Games-With-A-Purpose (GWAPs), and identify strengths and weaknesses of each trend. By doing so we aim to highlight particularities of each trend and to identify in which kind of settings one trend should be favored over the other two. On the other hand, we analyze the applicability of Collect4NLP approaches to the production of different types of NLP resources. We first list the types of NLP resources most used within its community and second propose a set of blueprints for mapping these resources to well-established language learning exercises as found in standard language learning textbooks.

In the field of citizen linguistics, various initiatives are aimed at the creation of language resources by members of the public. To recruit and retain these participants different incentives informed by different motivations, extrinsic and intrinsic ones, play a role at different project stages. Illustrated by a project in the field of lexicography which draws on the extrinsic and/or intrinsic motivation of participants, the complexity of providing the ‘right’ incentives is addressed. This complexity does not only surface when considering cultural differences and the heterogeneity of the motivations participants might have but also through the changing motivations over time. Here, identifying target groups may help to guide recruitment, retention and dissemination activities. In addition, continuous adaptations may be required during the course of the project to strike a balance between necessary and feasible incentives.