# Multilingual Culture-Independent Word Analogy Datasets

arXiv: https://arxiv.org/abs/1911.10038 · HTML: https://mlsanity.com/paper/1911.10038/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages](https://mlsanity.com/paper/2112.10553/) (2021)
- [Scientific and Creative Analogies in Pretrained Language Models](https://mlsanity.com/paper/2211.15268/) (2022)
- [Evaluation of contextual embeddings on less-resourced languages](https://mlsanity.com/paper/2107.10614/) (2021)
- [FrameNet Semantic Role Classification by Analogy](https://mlsanity.com/paper/2603.19825/) (2026)

## Abstract (arXiv)

In text processing, deep neural networks mostly use word embeddings as an input. Embeddings have to ensure that relations between words are reflected through distances in a high-dimensional numeric space. To compare the quality of different text embeddings, typically, we use benchmark datasets. We present a collection of such datasets for the word analogy task in nine languages: Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish. We redesigned the original monolingual analogy task to be much more culturally independent and also constructed cross-lingual analogy datasets for the involved languages. We present basic statistics of the created datasets and their initial evaluation using fastText embeddings.