In this work we train bilingual UWS models using the endangered language Mboshi as target and different well-resourced languages as aligned information. Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved. This might be due to the different language-dependent structures that are captured by using more than one language.  
 Question: How does the well-resourced language impact the quality of the output?
Results show that similar languages rank better in terms of segmentation performance, and that by combining the information learned by different models, segmentation is further improved.