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