In order to help with replication of the results in this paper, we have run the various morphological analysis steps and created the necessary training, tuning and test data files needed in order to train, tune and test any phrase-based machine translation system with our data.

We would particularly like to thank the developers of the open-source Moses machine translation toolkit and the Omorfi morphological analyzer for Finnish which we used for our experiments.

So, we ran the word-based baseline system, the segmented model (Unsup L—match), and the prediction model (CRF—LM) outputs, along with the reference translation through the supervised morphological analyzer Omorfi (Piri—nen and Listenmaa, 2007).

performance of unsupervised segmentation for translation, our third baseline is a segmented translation model based on a supervised segmentation model (called Sup), using the hand-built Omorfi morphological analyzer (Pirinen and Lis-tenmaa, 2007), which provided slightly higher BLEU scores than the word-based baseline.

Segmented translation performs morphological analysis on the morphologically complex text for use in the translation model (Brown et al., 1993; Goldwater and McClosky, 2005; de Gispert and Marifio, 2008).

Previous work in segmented translation has often used linguistically motivated morphological analysis selectively applied based on a language-specific heuristic.

In fact, in our experiments, unsupervised morphology always outperforms the use of a hand-built morphological analyzer .

In these cases, the joint model, entertaining all morphological possibilities, was able to find the combination of links and morphological analyses that are collectively more likely.

To date, studies of morphological analysis and dependency parsing have been pursued more or less independently.

Since space does not allow a full review of the vast literature on morphological analysis and parsing, we focus only on past research involving joint morphological and syntactic inference (§2.l); we then discuss Latin (§2.2), a language representative of the challenges that motivated our approach.