CoSTA / ST /inference /codes /german_synthetic_switching.py
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import random
import spacy
# Load the German spacy model
# nlp = spacy.load('de_core_news_sm')
# nlp = spacy.load('de_core_news_sm')
# nlp = spacy.load('tr_core_news_trf') #French
# nlp = spacy.load('hi_core_news_sm') #Greek
# nlp = spacy.load("fr_core_news_sm")
nlp = spacy.load('pt_core_news_sm')
# nlp = spacy.load('es_core_news_sm')
def load_german_english_dict(file_path):
"""
Load the German-English dictionary from a file.
Args:
- file_path (str): Path to the dictionary file.
Returns:
- dict: German-English dictionary.
"""
with open(file_path, 'r', encoding='utf-8') as file:
lines = file.readlines()
return {line.split()[0]: line.split()[1] for line in lines}
def translate_content_words(sentence, dictionary, probability=0.5):
"""
Randomly translate content words from German to English.
Args:
- sentence (str): German sentence to translate.
- dictionary (dict): Bilingual German-English dictionary.
- probability (float): Probability to translate a word.
Returns:
- str: Sentence with randomly translated content words.
"""
doc = nlp(sentence.lower())
translated_sentence = []
for token in doc:
# Check if the token is a content word
if token.pos_ in ['NOUN', 'VERB', 'ADJ', 'ADV', ]:
# Randomly decide whether to translate
if random.random() < probability:
# Translate if word is in the dictionary, otherwise keep the original word
translated_sentence.append(dictionary.get(token.text, token.text))
else:
translated_sentence.append(token.text)
else:
translated_sentence.append(token.text)
return ' '.join(translated_sentence)
# Load the dictionary from the file
german_english_dict = load_german_english_dict('Dictionary/portuguese_english_dict.txt')
# Example usage
sentence = "비ꡐ κ°€λŠ₯ν•œ μœ μ†μ„ μœ μ§€ν•  μˆ˜μžˆμ„ λ•Œ κ·Έ κ²°κ³Όκ°€ λ†’μŠ΅λ‹ˆλ‹€."
print(translate_content_words(sentence, german_english_dict, 0.5))
print(translate_content_words(sentence, german_english_dict, 0.8))
print(translate_content_words(sentence, german_english_dict, 1.0))