# Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures

arXiv: https://arxiv.org/abs/2206.04330 · HTML: https://mlsanity.com/paper/2206.04330/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Mapping Languages: The Corpus of Global Language Use](https://mlsanity.com/paper/2004.00798/) (2020)
- [Factors Influencing the Surprising Instability of Word Embeddings](https://mlsanity.com/paper/1804.09692/) (2018)
- [Representations of Language Varieties Are Reliable Given Corpus Similarity Measures](https://mlsanity.com/paper/2104.01294/) (2021)
- [Analyzing the Surprising Variability in Word Embedding Stability Across Languages](https://mlsanity.com/paper/2004.14876/) (2020)
- [The Influence of Down-Sampling Strategies on SVD Word Embedding Stability](https://mlsanity.com/paper/1808.06810/) (2018)
- [A German Corpus for Text Similarity Detection Tasks](https://mlsanity.com/paper/1703.03923/) (2017)

## Built on by (most cited first, 3 of 3)

- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [Variation and Instability in Dialect-Based Embedding Spaces](https://mlsanity.com/paper/2303.14963/) (2023)
- [Reliability of Topic Modeling](https://mlsanity.com/paper/2410.23186/) (2024)

## Abstract (arXiv)

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.