 How can neural models make sample-efficient generalizations from task--language combinations with available data to low-resource ones?
<fluency>  How can neural models make sample-efficient generalizations from task--language combinations with available data to low-resource ones?
 How can neural models make sample-efficient generalizations from task-language combinations with available data to low-resource ones?