Given a set of question-answer pairs, determine whether translating them into another language using a software like Google Translate would cause a loss of meaning. Additionally, evaluate the quality of the question-answer pairs.

Input: A list of question-answer pairs.
Output: For each pair, predict whether translating it would result in a loss of meaning. Also, provide an evaluation of the quality of the pairs.


question: "In the given Python code, how can we modify it to eliminate all the redundant adjectives and adverbs from this sentence: {\"text\": \"She quickly ran very quickly across the street\"}? We need to ensure that the syntax remains grammatically correct, and the meaning of the sentence is still conveyed effectively. Moreover, can you provide additional modifications to handle complex sentences with multiple clauses and varied tenses?"
answer: "To eliminate redundant adjectives and adverbs from the given sentence, we can use the Natural Language Toolkit (NLTK) library in Python. Here's the modified code:\n```\nimport nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nstop_words = set(stopwords.words('english'))\nlemmatizer = WordNetLemmatizer()\nsentence = \"She quickly ran very quickly across the street\"\ntokens = word_tokenize(sentence)\nfiltered_tokens = []\nfor token in tokens:\n if token.lower() not in stop_words:\n filtered_tokens.append(token)\npos_tags = nltk.pos_tag(filtered_tokens)\nnew_sentence = []\nfor word, tag in pos_tags:\n if tag not in ['RB', 'RBR', 'RBS', 'JJ', 'JJR', 'JJS']:\n new_sentence.append(word)\n elif len(new_sentence) == 0 or tag not in ['RB', 'RBR', 'RBS'] or pos_tags[pos_tags.index((word, tag)) - 1][1] not in ['RB', 'RBR', 'RBS']:\n new_sentence.append(word)\nnew_sentence = ' '.join(new_sentence)\nprint(new_sentence)\n```\nThis code removes all adjectives and adverbs that are redundant in the given sentence, while preserving the grammatical structure and meaning of the sentence. The output of this code will be: \"She ran across the street\".\nTo handle complex sentences with multiple clauses and varied tenses, we can use more advanced natural language processing techniques such as dependency parsing and named entity recognition. These techniques can help identify the relationships between words in a sentence and extract important information such as the subject, object, and verb. Here's an example code snippet that uses the spaCy library to handle complex sentences:\n```\nimport spacy\nnlp = spacy