# Using Multiple Subwords to Improve English-Esperanto Automated Literary Translation Quality

arXiv: https://arxiv.org/abs/2011.14190 · HTML: https://mlsanity.com/paper/2011.14190/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

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

- [An Analysis of Language Frequency and Error Correction for Esperanto](https://mlsanity.com/paper/2402.09696/) (2024)
- [Segmentation Beyond Defaults: Asymmetrical Byte Pair Encoding for Optimal Machine Translation Performance](https://mlsanity.com/paper/2511.03383/) (2025)
- [Open Machine Translation for Esperanto](https://mlsanity.com/paper/2603.29345/) (2026)

## Abstract (arXiv)

Building Machine Translation (MT) systems for low-resource languages remains challenging. For many language pairs, parallel data are not widely available, and in such cases MT models do not achieve results comparable to those seen with high-resource languages. When data are scarce, it is of paramount importance to make optimal use of the limited material available. To that end, in this paper we propose employing the same parallel sentences multiple times, only changing the way the words are split each time. For this purpose we use several Byte Pair Encoding models, with various merge operations used in their configuration. In our experiments, we use this technique to expand the available data and improve an MT system involving a low-resource language pair, namely English-Esperanto. As an additional contribution, we made available a set of English-Esperanto parallel data in the literary domain.