instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
question:
Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. 
 Question: Do the authors mention any confounds to their study?
answer:
No


question:
After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).  
 Question: what is the size of their dataset?
answer:
13,939


question:
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?
answer:

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.