DialogZoo / src /preprocess /MKQA_seperated .py
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data preprocessing update
a6326c7
import os
from utils import read_jsonl_file, write_jsonl_file, parse
'''
This script can divide the whole dataset into 26 parts via language
'''
def preprocess(args):
path = os.path.join(args.input_dir, "mkqa.jsonl")
data = read_jsonl_file(path)
'''
add add train/eval/test instruction and language chosen
'''
locales = list(data[0]["answers"].keys())
for locale in locales:
turns = []
for QA in data:
t = {"turn": "single",
"dialog": [],
"knowledge": None,
"goal": None,
"QA": None}
que = {"role": "ROLE1",
"utterance": QA["queries"][locale],
"utter_trans": QA["query"], # new feature
"slot_value_table": [],
"summary": None,
"locale": locale,
"scenario": None,
"intent": None,
"topic": None,
"answer": None}
# alternate answers
aliases = [] if "aliases" not in QA["answers"][locale][0] else QA["answers"][locale][0]["aliases"]
ans_svt = {"slot": QA["answers"][locale][0]["type"],
"value": QA["answers"][locale][0]["text"],
"act": None,
"aliases": aliases}
ans = {"role": "ROLE2",
"utterance": ans_svt["value"],
"utter_trans": QA["answers"]["en"][0]["text"], # new feature
"slot_value_table": ans_svt,
"summary": None,
"locale": locale,
"scenario": None,
"intent": None,
"topic": None,
"answer": ans_svt["value"]}
t["dialog"].append(que)
t["dialog"].append(ans)
turns.append(t)
write_jsonl_file(turns, os.path.join(args.output_dir, locale + ".jsonl"))
if __name__ == "__main__":
args = parse()
preprocess(args)