Add mockups
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wojbal2
parent
363e2ebca3
commit
3d9908b9ec
237
SDMockup.ipynb
237
SDMockup.ipynb
@ -10,7 +10,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -29,50 +29,255 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Nlu:\n",
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"class NaturalLanguageUnderstanding:\n",
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" acts: dict[list[str], str] = {\n",
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" ( \"potwierdzam\", \"dobrze\", \"ok\" ): \"ack\",\n",
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" (\"do widziena\", \"czesc\", \"koniec\", \"do zobaczenia\"): \"bye\",\n",
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" (\"cześć\", \"dzień dobry\", \"hello\", \"hej\"): \"hello\",\n",
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" (\"pomóc\", \"pomocy\", \"pomoc\"): \"help\",\n",
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" (\"zaprzeczam\", \"odrzucam\"): \"negate\",\n",
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" (\"alternatywny\", \"inne\", \"alternatywa\", \"inna\"): \"requalts\",\n",
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" (\"szczegółów\", \"informacji\", \"info\", \"informacje\"): \"reqmore\",\n",
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" (\"restart\"): \"restart\",\n",
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" (\"dziękuję\", \"dzięki\"): \"thankyou\",\n",
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" (\"tak\", \"chcę\"): \"confirm\",\n",
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" (\"nie chce\"): \"deny\",\n",
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" (\"basen\", \"parking\", \"śniadania\", \"osoby\"): \"inform\",\n",
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" (\"jaki\",\"?\", \"czy\", \"jak\", \"ile\", \"co\", \"gdzie\"): \"request\"\n",
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" }\n",
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" def __init__(self, text: str):\n",
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" self.text = text\n",
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" self.slot = []\n",
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" self.act = \"\"\n",
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" \n",
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" def get_dialog_act(self):\n",
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" act = \"question\" if \"?\" in self.text else \"\"\n",
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" if \"imię\" in self.text:\n",
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" self.slot.append((\"o_co\", \"imie\"))\n",
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" return {\"act\": act, \"slot\": self.slot }\n",
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" \n",
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" def get_dialog_act(self): \n",
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" for word in self.text.lower().split():\n",
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" for key in NaturalLanguageUnderstanding.acts:\n",
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" if word in key:\n",
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" self.act = NaturalLanguageUnderstanding.acts[key]\n",
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" return\n",
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" self.act = \"null\"\n",
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" \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'act': 'question', 'slot': [('o_co', 'imie')]}"
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"'request'"
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]
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},
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"execution_count": 15,
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nlu = Nlu(\"Jak masz na imię?\")\n",
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"nlu.get_dialog_act()"
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"nlu = NaturalLanguageUnderstanding(\"Jaki pokój proponujesz w tym hotelu?\")\n",
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"nlu.get_dialog_act()\n",
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"nlu.act"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# DST"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 53,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"class DialogueStateTracker:\n",
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" \n",
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" slots_dict: dict[tuple[str], str] = {\n",
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" (\"osoby\", \"ludzie\", \"osób\", \"osobowy\"): \"people\",\n",
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" (\"miasto\", \"miasta\", \"miejsowość\", \"poznań\", \"warszawa\", \"warszawie\", \"poznaniu\", \"kraków\", \"krakowie\"): \"city\",\n",
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" (\"basen\", \"parking\", \"śniadania\"): \"facilities\",\n",
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" (\"data\", \"datę\"): \"date\",\n",
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" (\"pokój\", \"pokoje\"): \"room\"\n",
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" }\n",
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" \n",
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" def __init__(self, nlu: NaturalLanguageUnderstanding):\n",
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" self.slots = []\n",
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" self.act = nlu.act\n",
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" self.text = nlu.text\n",
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" \n",
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" def get_dialog_slots(self):\n",
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" for word in self.text.lower().split():\n",
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" for key in DialogueStateTracker.slots_dict:\n",
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" if word in key:\n",
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" self.slots.append(DialogueStateTracker.slots_dict[key])\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['room']"
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]
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},
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"execution_count": 54,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dst: DialogueStateTracker = DialogueStateTracker(nlu)\n",
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"dst.get_dialog_slots()\n",
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"dst.slots\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Dialogue Policy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"class DialoguePolicy:\n",
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" user_act_to_system_act_dict: dict[str, str] = {\n",
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" \"ack\": \"reqmore\",\n",
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" \"bye\": \"bye\",\n",
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" \"hello\": \"welcomemsg\",\n",
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" \"help\": \"inform\",\n",
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" \"negate\": \"offer\",\n",
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" \"requalts\": \"offer\",\n",
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" \"reqmore\": \"inform\",\n",
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" \"restart\": \"welcomemsg\",\n",
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" \"thankyou\": \"reqmore\",\n",
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" \"confirm\": \"reqmore\",\n",
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" \"deny\": \"offer\",\n",
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" \"inform\": \"offer\",\n",
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" \"request\": \"inform\",\n",
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" \"null\": \"null\"\n",
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" }\n",
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" \n",
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" def __init__(self, dst: DialogueStateTracker):\n",
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" self.user_text = dst.text\n",
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" self.user_act = dst.act\n",
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" self.user_slots = dst.slots\n",
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" self.system_act = \"\"\n",
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" \n",
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" def get_system_act(self):\n",
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" self.system_act = DialoguePolicy.user_act_to_system_act_dict[self.user_act]\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'inform'"
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]
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},
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"execution_count": 46,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dp: DialoguePolicy = DialoguePolicy(dst)\n",
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"dp.get_system_act()\n",
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"dp.system_act"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NLG"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {},
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"outputs": [],
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"source": [
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"class NaturalLanguageGeneration:\n",
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" system_act_to_text = {\n",
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" \"reqmore\": \"Informuje więcej o \",\n",
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" \"bye\": \"Do widzenia\",\n",
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" \"welcomemsg\": \"Witaj w systemie rezerwacji hotelowych. W czym mogę pomóc?\",\n",
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" \"inform\": \"Informuje cię o \",\n",
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" \"offer\": \"Co myślisz o hotlu z \",\n",
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" \"reqmore\": \"Czy mogę jeszcze jakoś Ci pomóc?\",\n",
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" \"null\": \"\"\n",
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" }\n",
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" user_slots_to_text = {\n",
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" \"people\": \"pojemności pokoju\",\n",
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" \"city\": \"mieście\",\n",
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" \"facilities\": \"udogodnieniach\",\n",
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" \"date\": \"dacie\",\n",
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" \"room\": \"pokoju\"\n",
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" }\n",
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" \n",
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" def __init__(self, dp: DialoguePolicy):\n",
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" self.user_text = dp.user_text\n",
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" self.user_act = dp.user_act\n",
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" self.user_slots = dp.user_slots\n",
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" self.system_act = dp.system_act\n",
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" self.system_text = \"\"\n",
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" \n",
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" def generate_system_text(self):\n",
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" text: str = NaturalLanguageGeneration.system_act_to_text[self.system_act]\n",
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" slots_transformed = [NaturalLanguageGeneration.user_slots_to_text[slot] for slot in self.user_slots]\n",
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" self.system_text = text + \" i \".join(slots_transformed)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Informuje cię o pokoju'"
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]
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},
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"execution_count": 58,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nlg: NaturalLanguageGeneration = NaturalLanguageGeneration(dp)\n",
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"nlg.generate_system_text()\n",
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"nlg.system_text"
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]
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},
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{
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"cell_type": "code",
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