# On the Choice of Auxiliary Languages for Improved Sequence Tagging

arXiv: https://arxiv.org/abs/2005.09389 · HTML: https://mlsanity.com/paper/2005.09389/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 2 of 2)

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- [State Space Models for Extractive Summarization in Low Resource Scenarios](https://mlsanity.com/paper/2501.14673/) (2025)

## Abstract (arXiv)

Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models. In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language. Further, we show that attention-based meta-embeddings can effectively combine pre-trained embeddings from different languages for sequence tagging and set new state-of-the-art results for part-of-speech tagging in five languages.