# One model, two languages: training bilingual parsers with harmonized treebanks

arXiv: https://arxiv.org/abs/1507.08449 · HTML: https://mlsanity.com/paper/1507.08449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2015.

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

- [MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing](https://mlsanity.com/paper/2406.01052/) (2024)
- [Parsing with Pretrained Language Models, Multiple Datasets, and Dataset Embeddings](https://mlsanity.com/paper/2112.03625/) (2021)
- [Cross-lingual Inflection as a Data Augmentation Method for Parsing](https://mlsanity.com/paper/2205.09350/) (2022)

## Abstract (arXiv)

We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both. We test the approach on the Universal Dependency Treebanks, training with MaltParser and MaltOptimizer. The results show that these bilingual parsers are more than competitive, as most combinations not only preserve accuracy, but some even achieve significant improvements over the corresponding monolingual parsers. Preliminary experiments also show the approach to be promising on texts with code-switching and when more languages are added.