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5.08 kB
| from utils import ( | |
| write_jsonl_file, | |
| parse, | |
| ) | |
| import os | |
| topics = { | |
| 1: "Ordinary Life", | |
| 2: "School Life", | |
| 3: "Culture & Education", | |
| 4: "Attitude & Emotion", | |
| 5: "Relationship", | |
| 6: "Tourism", | |
| 7: "Health", | |
| 8: "Work", | |
| 9: "Politics", | |
| 10: "Finance", | |
| } | |
| emotions = { | |
| 0: "neutral", | |
| 1: "anger", | |
| 2: "disgust", | |
| 3: "fear", | |
| 4: "happiness", | |
| 5: "sadness", | |
| 6: "surprise", | |
| } | |
| acts = {1: "inform", 2: "question", 3: "directive", 4: "commissive"} | |
| def load_topics(args): | |
| text_file = os.path.join(args.input_dir, "dialogues_text.txt") | |
| topic_file = os.path.join(args.input_dir, "dialogues_topic.txt") | |
| text2topic = dict() | |
| with open(text_file, "r", encoding="utf-8") as text_reader, open( | |
| topic_file, "r", encoding="utf-8" | |
| ) as topic_reader: | |
| for line in text_reader: | |
| text = line.strip() | |
| topic = topics[int(topic_reader.readline().strip())] | |
| # if text in text2topic and text not in [ | |
| # "Can I help you ? __eou__ I hope so . I'm looking for some material for a paper I'm writing , and I'm not quite sure where to look . __eou__ I'll certainly try to help you . What topic is your paper on ? __eou__ My paper is on the influence of television on children . __eou__ There are several possible sources you might use for that topic . I suggest you use the computer and the computer will give you a list of every scientific journal that talks about children and television . __eou__ Thank you for you help . __eou__" | |
| # "Hey , Ann . You don't have a pen , do you ? __eou__ Sure , here you go . __eou__ Thanks . I don't suppose you have some paper , too . __eou__ Of course . There you are . __eou__ Thanks so much . I owe you one ." | |
| # ]: | |
| # print(text, topic, text2topic[text]) | |
| # assert text2topic[text] == topic | |
| text2topic[text] = topic | |
| return text2topic | |
| def preprocess(args, split, text2topic): | |
| input_dir = os.path.join(args.input_dir, split) | |
| text_file = os.path.join(input_dir, f"dialogues_{split}.txt") | |
| act_file = os.path.join(input_dir, f"dialogues_act_{split}.txt") | |
| emotion_file = os.path.join(input_dir, f"dialogues_emotion_{split}.txt") | |
| if split == "validation": | |
| split = "dev" | |
| outfile = os.path.join(args.output_dir, f"{split}.jsonl") | |
| processed_data = [] | |
| with open(text_file, "r", encoding="utf-8") as text_reader, open( | |
| act_file, "r", encoding="utf-8" | |
| ) as act_reader, open(emotion_file, "r", encoding="utf-8") as emotion_reader: | |
| for line in text_reader: | |
| text = line.strip() | |
| if text in text2topic: | |
| topic = text2topic[text] | |
| else: | |
| _text = "Sam , can we stop at this bicycle shop ? __eou__ Do you want to buy a new bicycle ? __eou__ Yes , and they have a sale on now . __eou__ What happened to your old one ? __eou__ I left it at my parent's house , but I need one here as well . __eou__ I've been using Jim's old bike but he needs it back . __eou__ Let's go then . __eou__ Look at this mountain bike . It is only £ 330 . Do you like it ? __eou__ I prefer something like this one - a touring bike , but it is more expensive . __eou__ How much is it ? __eou__ The price on the tag says £ 565 but maybe you can get a discount . __eou__ OK , let's go and ask . __eou__" | |
| topic = text2topic[_text] | |
| utterances = text.split("__eou__") | |
| assert not utterances[-1] | |
| utterances = utterances[:-1] | |
| _acts = list( | |
| map(lambda x: acts[int(x)], act_reader.readline().strip().split()) | |
| ) | |
| _emotions = list( | |
| map( | |
| lambda x: emotions[int(x)], | |
| emotion_reader.readline().strip().split(), | |
| ) | |
| ) | |
| dialogue = { | |
| "turn": "multi", | |
| "locale": "en", | |
| "domain": [topic], | |
| "dialog": [], | |
| "knowledge": {"type": "list", "value": sorted(emotions.values())}, | |
| } | |
| assert len(utterances) == len(_acts) and len(utterances) == len( | |
| _emotions | |
| ), f"{utterances}\n{_acts}\n{_emotions}" | |
| roles = ["ROLE1", "ROLE2"] | |
| for idx, utterance in enumerate(utterances): | |
| assert utterance | |
| dialogue["dialog"].append( | |
| { | |
| "roles": [roles[idx % 2]], | |
| "utterance": utterance, | |
| "active_intents": [_acts[idx]], | |
| "emotions": [{"emotion": _emotions[idx]}], | |
| } | |
| ) | |
| processed_data.append(dialogue) | |
| write_jsonl_file(processed_data, outfile) | |
| if __name__ == "__main__": | |
| args = parse() | |
| text2topic = load_topics(args) | |
| preprocess(args, "train", text2topic) | |
| preprocess(args, "validation", text2topic) | |
| preprocess(args, "test", text2topic) | |