Gamified crowdsourcing for idiom corpora construction

• Published in: Natural language engineering Vol. 29; no. 4; pp. 909 - 941
• Main Authors: Eryiğit, GülŞen, Şentaş, Ali, Monti, Johanna
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.07.2023
• Subjects: Annotations; Artificial intelligence; Crowdsourcing; Dictionaries; Foreign language learning; Gamification; Identification; Idioms; Keywords; Language; Learning; Lexicography; Machine translation; Morphology; Natural language; Natural language processing; Parsing; Second language learning; Crowdsourcing; Idiomatic expressions; Gamification; Game with a purpose (GWAP); Language resources
• ISSN: 1351-3249; 1469-8110
• DOI: 10.1017/S1351324921000401

Learning idiomatic expressions is seen as one of the most challenging stages in second-language learning because of their unpredictable meaning. A similar situation holds for their identification within natural language processing applications such as machine translation and parsing. The lack of high-quality usage samples exacerbates this challenge not only for humans but also for artificial intelligence systems. This article introduces a gamified crowdsourcing approach for collecting language learning materials for idiomatic expressions; a messaging bot is designed as an asynchronous multiplayer game for native speakers who compete with each other while providing idiomatic and nonidiomatic usage examples and rating other players’ entries. As opposed to classical crowd-processing annotation efforts in the field, for the first time in the literature, a crowd-creating & crowd-rating approach is implemented and tested for idiom corpora construction. The approach is language-independent and evaluated on two languages in comparison to traditional data preparation techniques in the field. The reaction of the crowd is monitored under different motivational means (namely, gamification affordances and monetary rewards). The results reveal that the proposed approach is powerful in collecting the targeted materials, and although being an explicit crowdsourcing approach, it is found entertaining and useful by the crowd. The approach has been shown to have the potential to speed up the construction of idiom corpora for different natural languages to be used as second-language learning material, training data for supervised idiom identification systems, or samples for lexicographic studies.