instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used. However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories.
answer:
What are the weaknesses of their proposed interpretability quantification method?


question:
In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 .  Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign)
answer:
Do they perform some annotation?


question:
For all tasks, we use a TensorFlow implementation. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model. 
answer:

Did they experiment on all the tasks?