Around the world in 60 words: A generative vocabulary test for online research
This provides a scalable tool for researchers conducting cross-cultural and linguistic studies online, addressing a bottleneck in validating language proficiency.
The paper tackles the problem of validating self-reported language proficiency in online experiments by developing an automated pipeline to generate vocabulary tests using Wikipedia text, which samples rare nouns and creates pseudowords. Results from six experiments across eight languages show the test can distinguish native speakers of closely related languages, is reliable (r=0.82), and correlates with existing tests and self-reports.
Conducting experiments with diverse participants in their native languages can uncover insights into culture, cognition, and language that may not be revealed otherwise. However, conducting these experiments online makes it difficult to validate self-reported language proficiency. Furthermore, existing proficiency tests are small and cover only a few languages. We present an automated pipeline to generate vocabulary tests using text from Wikipedia. Our pipeline samples rare nouns and creates pseudowords with the same low-level statistics. Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0.82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports. We further show that test accuracy is negatively correlated with the linguistic distance between the tested and the native language. Our test, available in eight languages, can easily be extended to other languages.