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:
Participants who were shown the definition were more likely to suggest to ban the tweet. In fact, participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%). This suggests that participants in that group aligned their own opinion with the definition.
answer:
How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?


question:
Recently, MADAMIRA BIBREF6 system has been evaluated using a blind testset (25K words for Modern Standard Arabic (MSA) selected from Penn Arabic Tree bank (PATB)), and the reported accuracy was 96.2% as the percentage of words where the chosen analysis (provided by SAMA morphological analyzer BIBREF7 ) has the correct lemma. As MSA is usually written without diacritics and IR systems normally remove all diacritics from search queries and indexed data as a basic preprocessing step, so another column for undiacritized lemma is added and it's used for evaluating our lemmatizer and comparing with state-of-the-art system for lemmatization; MADAMIRA.
answer:
What is the state of the art?


question:
 We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions). It delivers frame-by-frame scores ($\in [0;100]$) for discrete emotional states of joy, anger and fear. We extract the audio signal for the same sequence as described for facial expressions and apply an off-the-shelf tool for emotion recognition. The software delivers single classification scores for a set of 24 discrete emotions for the entire utterance.
answer:

What are the emotion detection tools used for audio and face input?