your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Teacher:Given an input stream, the objective of this task is to classify whether words in the stream are grammatically correct or not. The input to this task is a stream of words, possibly from captions generated by a speech-to-text engine, and the output is a classification of each word from the labels (reason) = [NO_DIFF (correct), CASE_DIFF (case error), PUNCUATION_DIFF (punctuation error), CASE_AND_PUNCUATION_DIFF (both case and punctuation error), STEM_BASED_DIFF (stem word error), DIGIT_DIFF (digit error), INTRAWORD_PUNC_DIFF (intra-word punctuation error), and UNKNOWN_TYPE_DIFF (an error that does not corrrespond to the previous categories)].
Teacher: Now, understand the problem? Solve this instance: ['ionization', 'energy', 'refers', 'to', 'the', 'energy', "that's", 'required', 'to', 'remove', 'an', 'electron', 'from', 'a', 'neutral', 'atom', 'so', 'if', 'we', 'look', 'down', 'here', 'this', 'a', 'represents', 'a', 'neutral', 'atom', 'meaning', 'equal', 'numbers', 'of', 'protons', 'and', 'electrons', 'and', 'since', 'the', 'positively', 'charged', 'nucleus', 'is', 'going', 'to', 'attract', 'those', 'negatively', 'charged', 'electrons', "it's", 'going', 'to', 'take', 'energy', 'to', 'pull', 'an', 'electron', 'away', 'from', 'that', 'attractive', 'force', 'of', 'the', 'nucleus', 'and', 'so', "that's", 'your', 'ionization', 'energy', 'if', 'you', 'take', 'away', 'an', 'electron', 'you', 'no', 'longer', 'have', 'equal', 'numbers', 'of', 'protons', 'and', 'electrons', 'you', 'have', 'one', 'more', 'proton', 'than', 'you', 'do', 'electrons', 'so', 'you', 'get']
Student:
Answer: To solve this problem and classify the given words, we need to follow these steps:

1. Read the input stream (list of words)
2. Initialize an empty list called "output_labels"
3. For each word in the input stream, classify it based on the given categories (NO_DIFF, CASE_DIFF, etc.) and determine its label.
4. Add the label for each word to the "output_labels" list

Given the instance provided:

1. Read the input stream: ['ionization', 'energy', 'refers', ...]

2. Initialize an empty list called "output_labels": []

3. Classify each word in the input stream:

   - ionization: NO_DIFF (correct)
   - energy: NO_DIFF (correct)
   - refers: NO_DIFF (correct)
   ...
   - that's: INTRAWORD_PUNC_DIFF (intra-word punctuation error, since there is an apostrophe within the word)
   ...
   - it's: INTRAWORD_PUNC_DIFF (intra-word punctuation error, since there is an apostrophe within the word)
   ...

4. Add the determined label for each word to the output_labels list:

    ['NO_DIFF', 'NO_DIFF', 'NO_DIFF', ..., 'INTRAWORD_PUNC_DIFF', ..., 'INTRAWORD_PUNC_DIFF', ...]

Final output:

[NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, INTRAWORD_PUNC_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, INTRAWORD_PUNC_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, INTRAWORD_PUNC_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF, NO_DIFF]