Corpus similarity measures remain robust across diverse languages

Li, H. & Dunn, J. (2022). “Corpus similarity measures remain robust across diverse languages.” Lingua. Abstract. This paper experiments with frequency-based corpus similarity measures across 39 languages using a register prediction task. The goal is to quantify (i) the distance between different corpora from the same language and (ii) the homogeneity of individual corpora. Both … More Corpus similarity measures remain robust across diverse languages

Predicting Embedding Reliability in Low-Resource Settings

Dunn, J.; Li, H.; & Sastre, D. (2022). “Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures.” In Proceedings of the 13th International Conference on Language Resources and Evaluation. European Language Resources Association. 6461-6470. Abstract This paper simulates a low-resource setting across 17 languages in order to evaluate embedding similarity, stability, and reliability under … More Predicting Embedding Reliability in Low-Resource Settings