src/preprocess/DailyDialog.py

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)