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I declare resumed the session of the European Parliament adjourned on Friday 17 December 1999, and I would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period.Reprise de la session Je déclare reprise la session du Parlement européen qui avait été interrompue le vendredi 17 décembre dernier et je vous renouvelle tous mes vux en espérant que vous avez passé de bonnes vacances.Comme vous avez pu le constater, le grand "bogue de l'an 2000" ne s'est pas produit.En revanche, les citoyens d'un certain nombre de nos pays ont été victimes de catastrophes naturelles qui ont vraiment été terribles.Altogether, the corpus comprises of about 30 million words for each of the 11 official languages of the European Union — Europarl: A Parallel Corpus for Statistical Machine Translation, 2005.The raw data is available on the European Parliament website in HTML format.

En attendant, je souhaiterais, comme un certain nombre de collègues me l'ont demandé, que nous observions une minute de silence pour toutes les victimes, des tempêtes notamment, dans les différents pays de l'Union européenne qui ont été touchés. In this case, we will use UTF-8 that will easily handle the unicode characters in both files.

Looking at some samples of text, some minimal text cleaning may include: # clean a list of lines def clean_lines(lines): cleaned = list() # prepare regex for char filtering re_print = re.compile('[^%s]' % re.escape(string.printable)) # prepare translation table for removing punctuation table = str.maketrans('', '', string.punctuation) for line in lines: # normalize unicode characters line = normalize('NFD', line).encode('ascii', 'ignore') line = line.decode('UTF-8') # tokenize on white space line = line.split() # convert to lower case line = [word.lower() for word in line] # remove punctuation from each token line = [word.translate(table) for word in line] # remove non-printable chars form each token line = [re_print.sub('', w) for w in line] # remove tokens with numbers in them line = [word for word in line if word.isalpha()] # store as string cleaned.append(' '.join(line)) return cleaned Once normalized, we save the lists of clean lines directly in binary format using the pickle API.

This will speed up loading for further operations later and in the future.

The function below, named # load doc into memory def load_doc(filename): # open the file as read only file = open(filename, mode='rt', encoding='utf-8') # read all text text = file.read() # close the file file.close() return text # load doc into memory def load_doc(filename): # open the file as read only file = open(filename, mode='rt', encoding='utf-8') # read all text text = file.read() # close the file file.close() return text # split a loaded document into sentences def to_sentences(doc): return doc.strip().split('\n') # shortest and longest sentence lengths def sentence_lengths(sentences): lengths = [len(s.split()) for s in sentences] return min(lengths), max(lengths) # load English data filename = 'europarl-v7.fr-en.en' doc = load_doc(filename) sentences = to_sentences(doc) minlen, maxlen = sentence_lengths(sentences) print('English data: sentences=%d, min=%d, max=%d' % (len(sentences), minlen, maxlen)) # load French data filename = 'europarl-v7.fr-en.fr' doc = load_doc(filename) sentences = to_sentences(doc) minlen, maxlen = sentence_lengths(sentences) print('French data: sentences=%d, min=%d, max=%d' % (len(sentences), minlen, maxlen)) Importantly, we can see that the number of lines 2,007,723 matches the expectation.

The data needs some minimal cleaning before being used to train a neural translation model.

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