CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
This addresses the need for more representative multilingual corpora by providing a key resource for under-served languages, though it is incremental as it builds on existing evaluation practices.
The paper tackles the problem of poor language identification (LID) performance on noisy web data by introducing CommonLID, a human-annotated benchmark covering 109 languages, and shows that existing evaluations overestimate accuracy for many languages in this domain.
Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heterogeneous web data often used to train multilingual language models. In this paper, we introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. Many of the included languages have been previously under-served, making CommonLID a key resource for developing more representative high-quality text corpora. We show CommonLID's value by using it, alongside five other common evaluation sets, to test eight popular LID models. We analyse our results to situate our contribution and to provide an overview of the state of the art. In particular, we highlight that existing evaluations overestimate LID accuracy for many languages in the web domain. We make CommonLID and the code used to create it available under an open, permissive license.