Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures
It addresses the challenge of evaluating embeddings for low-resource languages where data limitations hinder systematic validation, though it is incremental as it applies existing measures to new settings.
This paper tackled the problem of predicting embedding reliability in low-resource languages by showing that upstream corpus similarity measures can forecast downstream embedding similarity, with results demonstrating robust estimation on small data across 17 languages.
This paper simulates a low-resource setting across 17 languages in order to evaluate embedding similarity, stability, and reliability under different conditions. The goal is to use corpus similarity measures before training to predict properties of embeddings after training. The main contribution of the paper is to show that it is possible to predict downstream embedding similarity using upstream corpus similarity measures. This finding is then applied to low-resource settings by modelling the reliability of embeddings created from very limited training data. Results show that it is possible to estimate the reliability of low-resource embeddings using corpus similarity measures that remain robust on small amounts of data. These findings have significant implications for the evaluation of truly low-resource languages in which such systematic downstream validation methods are not possible because of data limitations.