# A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages

arXiv: https://arxiv.org/abs/1911.08117 · HTML: https://mlsanity.com/paper/1911.08117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Analyzing the Use of Character-Level Translation with Sparse and Noisy Datasets](https://mlsanity.com/paper/2109.13723/) (2021)

## Abstract (arXiv)

We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the basic unit of translation is the morpheme, but word boundaries are respected at all stages of the translation process. Our model extends the classic phrase-based model by means of (1) word boundary-aware morpheme-level phrase extraction, (2) minimum error-rate training for a morpheme-level translation model using word-level BLEU, and (3) joint scoring with morpheme- and word-level language models. Further improvements are achieved by combining our model with the classic one. The evaluation on English to Finnish using Europarl (714K sentence pairs; 15.5M English words) shows statistically significant improvements over the classic model based on BLEU and human judgments.