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from zipfile import ZipFile, ZIP_DEFLATED |
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from shutil import rmtree |
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import json |
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import os |
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from tqdm import tqdm |
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from collections import Counter |
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from pprint import pprint |
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from nltk.tokenize import sent_tokenize, word_tokenize |
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from nltk.tokenize.treebank import TreebankWordDetokenizer |
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import re |
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topic_map = { |
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1: "Ordinary Life", |
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2: "School Life", |
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3: "Culture & Education", |
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4: "Attitude & Emotion", |
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5: "Relationship", |
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6: "Tourism", |
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7: "Health", |
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8: "Work", |
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9: "Politics", |
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10: "Finance" |
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} |
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act_map = { |
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1: "inform", |
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2: "question", |
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3: "directive", |
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4: "commissive" |
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} |
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emotion_map = { |
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0: "no emotion", |
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1: "anger", |
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2: "disgust", |
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3: "fear", |
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4: "happiness", |
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5: "sadness", |
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6: "surprise" |
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} |
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def preprocess(): |
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original_data_dir = 'ijcnlp_dailydialog' |
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new_data_dir = 'data' |
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if not os.path.exists(original_data_dir): |
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original_data_zip = 'ijcnlp_dailydialog.zip' |
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if not os.path.exists(original_data_zip): |
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raise FileNotFoundError(f'cannot find original data {original_data_zip} in dailydialog/, should manually download ijcnlp_dailydialog.zip from http://yanran.li/files/ijcnlp_dailydialog.zip') |
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else: |
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archive = ZipFile(original_data_zip) |
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archive.extractall() |
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os.makedirs(new_data_dir, exist_ok=True) |
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dataset = 'dailydialog' |
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splits = ['train', 'validation', 'test'] |
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dialogues_by_split = {split:[] for split in splits} |
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dial2topics = {} |
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with open(os.path.join(original_data_dir, 'dialogues_text.txt')) as dialog_file, \ |
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open(os.path.join(original_data_dir, 'dialogues_topic.txt')) as topic_file: |
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for dialog, topic in zip(dialog_file, topic_file): |
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topic = int(topic.strip()) |
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dialog = dialog.replace(' __eou__ ', ' ') |
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if dialog in dial2topics: |
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dial2topics[dialog].append(topic) |
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else: |
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dial2topics[dialog] = [topic] |
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global topic_map, act_map, emotion_map |
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ontology = {'domains': {x:{'description': '', 'slots': {}} for x in topic_map.values()}, |
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'intents': {x:{'description': ''} for x in act_map.values()}, |
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'state': {}, |
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'dialogue_acts': { |
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"categorical": [], |
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"non-categorical": [], |
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"binary": {} |
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}} |
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detokenizer = TreebankWordDetokenizer() |
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for data_split in splits: |
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archive = ZipFile(os.path.join(original_data_dir, f'{data_split}.zip')) |
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with archive.open(f'{data_split}/dialogues_{data_split}.txt') as dialog_file, \ |
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archive.open(f'{data_split}/dialogues_act_{data_split}.txt') as act_file, \ |
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archive.open(f'{data_split}/dialogues_emotion_{data_split}.txt') as emotion_file: |
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for dialog_line, act_line, emotion_line in tqdm(zip(dialog_file, act_file, emotion_file)): |
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if not dialog_line.strip(): |
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break |
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utts = dialog_line.decode().split("__eou__")[:-1] |
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acts = act_line.decode().split(" ")[:-1] |
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emotions = emotion_line.decode().split(" ")[:-1] |
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assert (len(utts) == len(acts) == len(emotions)), "Different turns btw dialogue & emotion & action" |
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topics = dial2topics[dialog_line.decode().replace(' __eou__ ', ' ')] |
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topic = Counter(topics).most_common(1)[0][0] |
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domain = topic_map[topic] |
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dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}' |
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dialogue = { |
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'dataset': dataset, |
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'data_split': data_split, |
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'dialogue_id': dialogue_id, |
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'original_id': f'{data_split}-{len(dialogues_by_split[data_split])}', |
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'domains': [domain], |
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'turns': [] |
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} |
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for utt, act, emotion in zip(utts, acts, emotions): |
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speaker = 'user' if len(dialogue['turns']) % 2 == 0 else 'system' |
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intent = act_map[int(act)] |
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emotion = emotion_map[int(emotion)] |
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utt = ' '.join([detokenizer.detokenize(word_tokenize(s)) for s in sent_tokenize(utt)]) |
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utt = utt.replace(' ’ ', "'") |
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utt = re.sub('\.(?!com)(\w)', lambda x: '. '+x.group(1), utt) |
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dialogue['turns'].append({ |
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'speaker': speaker, |
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'utterance': utt.strip(), |
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'utt_idx': len(dialogue['turns']), |
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'dialogue_acts': { |
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'binary': [{ |
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'intent': intent, |
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'domain': '', |
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'slot': '' |
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}], |
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'categorical': [], |
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'non-categorical': [], |
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}, |
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'emotion': emotion, |
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}) |
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ontology["dialogue_acts"]['binary'].setdefault((intent, '', ''), {}) |
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ontology["dialogue_acts"]['binary'][(intent, '', '')][speaker] = True |
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dialogues_by_split[data_split].append(dialogue) |
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ontology["dialogue_acts"]['binary'] = sorted([str({'user': speakers.get('user', False), 'system': speakers.get('system', False), 'intent':da[0],'domain':da[1], 'slot':da[2]}) for da, speakers in ontology["dialogue_acts"]['binary'].items()]) |
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dialogues = dialogues_by_split['train']+dialogues_by_split['validation']+dialogues_by_split['test'] |
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json.dump(dialogues[:10], open(f'dummy_data.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False) |
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json.dump(ontology, open(f'{new_data_dir}/ontology.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False) |
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json.dump(dialogues, open(f'{new_data_dir}/dialogues.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False) |
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with ZipFile('data.zip', 'w', ZIP_DEFLATED) as zf: |
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for filename in os.listdir(new_data_dir): |
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zf.write(f'{new_data_dir}/{filename}') |
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rmtree(original_data_dir) |
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rmtree(new_data_dir) |
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return dialogues, ontology |
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if __name__ == '__main__': |
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preprocess() |
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