528 lines
24 KiB
Plaintext
528 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Systemy Dialogowe </h1>\n",
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"<h2> 9. <i>Zarz\u0105dzanie dialogiem z wykorzystaniem regu\u0142</i> [laboratoria]</h2> \n",
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"<h3> Marek Kubis (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zarz\u0105dzanie dialogiem z wykorzystaniem regu\u0142\n",
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"============================================\n",
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"\n",
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"Agent dialogowy wykorzystuje do zarz\u0105dzanie dialogiem dwa modu\u0142y:\n",
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"\n",
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" - monitor stanu dialogu (dialogue state tracker, DST) \u2014 modu\u0142 odpowiedzialny za \u015bledzenie stanu dialogu.\n",
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"\n",
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" - taktyk\u0119 prowadzenia dialogu (dialogue policy) \u2014 modu\u0142, kt\u00f3ry na podstawie stanu dialogu\n",
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" podejmuje decyzj\u0119 o tym jak\u0105 akcj\u0119 (akt systemu) agent ma podj\u0105\u0107 w kolejnej turze.\n",
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"\n",
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"Oba modu\u0142y mog\u0105 by\u0107 realizowane zar\u00f3wno z wykorzystaniem regu\u0142 jak i uczenia maszynowego.\n",
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"Mog\u0105 one zosta\u0107 r\u00f3wnie\u017c po\u0142\u0105czone w pojedynczy modu\u0142 zwany w\u00f3wczas *mened\u017cerem dialogu*.\n",
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"\n",
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"Przyk\u0142ad\n",
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"--------\n",
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"\n",
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"Zaimplementujemy regu\u0142owe modu\u0142y monitora stanu dialogu oraz taktyki dialogowej a nast\u0119pnie\n",
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"osadzimy je w \u015brodowisku *[ConvLab-2](https://github.com/thu-coai/ConvLab-2)*,\n",
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"kt\u00f3re s\u0142u\u017cy do ewaluacji system\u00f3w dialogowych.\n",
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"\n",
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"**Uwaga:** Niekt\u00f3re modu\u0142y \u015brodowiska *ConvLab-2* nie s\u0105 zgodne z najnowszymi wersjami Pythona,\n",
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"dlatego przed uruchomieniem poni\u017cszych przyk\u0142ad\u00f3w nale\u017cy si\u0119 upewni\u0107, \u017ce maj\u0105 Pa\u0144stwo interpreter\n",
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"Pythona w wersji 3.7. W przypadku nowszych wersji Ubuntu Pythona 3.7 mo\u017cna zainstalowa\u0107 z\n",
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"repozytorium `deadsnakes`, wykonuj\u0105c polecenia przedstawione poni\u017cej.\n",
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"\n",
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"```\n",
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"sudo add-apt-repository ppa:deadsnakes/ppa\n",
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"sudo apt update\n",
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"sudo apt install python3.7 python3.7-dev python3.7-venv\n",
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"```\n",
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"\n",
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"W przypadku innych system\u00f3w mo\u017cna skorzysta\u0107 np. z narz\u0119dzia [pyenv](https://github.com/pyenv/pyenv) lub \u015brodowiska [conda](https://conda.io).\n",
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"\n",
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"Ze wzgl\u0119du na to, \u017ce *ConvLab-2* ma wiele zale\u017cno\u015bci zach\u0119cam r\u00f3wnie\u017c do skorzystania ze \u015brodowiska\n",
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"wirtualnego `venv`, w kt\u00f3rym modu\u0142y zale\u017cne mog\u0105 zosta\u0107 zainstalowane.\n",
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"W tym celu nale\u017cy wykona\u0107 nast\u0119puj\u0105ce polecenia\n",
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"\n",
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"```\n",
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"python3.7 -m venv convenv # utworzenie nowego \u015brodowiska o nazwie convenv\n",
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"source convenv/bin/activate # aktywacja \u015brodowiska w bie\u017c\u0105cej pow\u0142oce\n",
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"pip install --ignore-installed jupyter # instalacja jupytera w \u015brodowisku convenv\n",
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"```\n",
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"\n",
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"Po skonfigurowaniu \u015brodowiska mo\u017cna przyst\u0105pi\u0107 do instalacji *ConvLab-2*, korzystaj\u0105c z\n",
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"nast\u0119puj\u0105cych polece\u0144\n",
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"\n",
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"```\n",
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"mkdir -p l08\n",
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"cd l08\n",
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"git clone https://github.com/thu-coai/ConvLab-2.git\n",
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"cd ConvLab-2\n",
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"pip install -e .\n",
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"python -m spacy download en_core_web_sm\n",
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"cd ../..\n",
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"```\n",
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"\n",
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"Po zako\u0144czeniu instalacji nale\u017cy ponownie uruchomi\u0107 notatnik w pow\u0142oce, w kt\u00f3rej aktywne jest\n",
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"\u015brodowisko wirtualne *convenv*.\n",
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"\n",
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"```\n",
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"jupyter notebook 08-zarzadzanie-dialogiem-reguly.ipynb\n",
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"```\n",
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"\n",
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"Dzia\u0142anie zaimplementowanych modu\u0142\u00f3w zilustrujemy, korzystaj\u0105c ze zbioru danych\n",
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"[MultiWOZ](https://github.com/budzianowski/multiwoz) (Budzianowski i in., 2018), kt\u00f3ry zawiera\n",
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"wypowiedzi dotycz\u0105ce m.in. rezerwacji pokoi hotelowych, zamawiania bilet\u00f3w kolejowych oraz\n",
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"rezerwacji stolik\u00f3w w restauracji.\n",
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"\n",
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"### Monitor Stanu Dialogu\n",
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"\n",
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"Do reprezentowania stanu dialogu u\u017cyjemy struktury danych wykorzystywanej w *ConvLab-2*."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from convlab2.util.multiwoz.state import default_state\n",
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"default_state()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Metoda `update` naszego monitora stanu dialogu b\u0119dzie przyjmowa\u0107 akty u\u017cytkownika i odpowiednio\n",
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"modyfikowa\u0107 stan dialogu.\n",
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"W przypadku akt\u00f3w typu `inform` warto\u015bci slot\u00f3w zostan\u0105 zapami\u0119tane w s\u0142ownikach odpowiadaj\u0105cych\n",
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"poszczeg\u00f3lnym dziedzinom pod kluczem `belief_state`.\n",
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"W przypadku akt\u00f3w typu `request` sloty, o kt\u00f3re pyta u\u017cytkownik zostan\u0105 zapisane pod kluczem\n",
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"`request_state`.\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import os\n",
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"from convlab2.dst.dst import DST\n",
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"from convlab2.dst.rule.multiwoz.dst_util import normalize_value\n",
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"from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA\n",
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"\n",
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"\n",
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"class SimpleRuleDST(DST):\n",
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" def __init__(self):\n",
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" DST.__init__(self)\n",
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" self.state = default_state()\n",
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" self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\n",
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"\n",
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" def update(self, user_act=None):\n",
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" for intent, domain, slot, value in user_act:\n",
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" domain = domain.lower()\n",
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" intent = intent.lower()\n",
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"\n",
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" if domain in ['unk', 'general', 'booking']:\n",
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" continue\n",
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"\n",
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" if intent == 'inform':\n",
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" k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
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"\n",
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" if k is None:\n",
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" continue\n",
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"\n",
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" domain_dic = self.state['belief_state'][domain]\n",
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"\n",
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" if k in domain_dic['semi']:\n",
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" nvalue = normalize_value(self.value_dict, domain, k, value)\n",
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" self.state['belief_state'][domain]['semi'][k] = nvalue\n",
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" elif k in domain_dic['book']:\n",
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" self.state['belief_state'][domain]['book'][k] = value\n",
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" elif k.lower() in domain_dic['book']:\n",
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" self.state['belief_state'][domain]['book'][k.lower()] = value\n",
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" elif intent == 'request':\n",
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" k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
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"\n",
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" if domain not in self.state['request_state']:\n",
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" self.state['request_state'][domain] = {}\n",
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" if k not in self.state['request_state'][domain]:\n",
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" self.state['request_state'][domain][k] = 0\n",
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"\n",
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" return self.state\n",
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"\n",
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" def init_session(self):\n",
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" self.state = default_state()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"W definicji metody `update` zak\u0142adamy, \u017ce akty dialogowe przekazywane do monitora stanu dialogu z\n",
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"modu\u0142u NLU s\u0105 czteroelementowymi listami z\u0142o\u017conymi z:\n",
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"\n",
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" - nazwy aktu u\u017cytkownika,\n",
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" - nazwy dziedziny, kt\u00f3rej dotyczy wypowied\u017a,\n",
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" - nazwy slotu,\n",
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" - warto\u015bci slotu.\n",
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"\n",
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"Zobaczmy na kilku prostych przyk\u0142adach jak stan dialogu zmienia si\u0119 pod wp\u0142ywem przekazanych akt\u00f3w\n",
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"u\u017cytkownika."
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"dst = SimpleRuleDST()\n",
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"dst.state"
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"dst.update([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])\n",
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"dst.state['belief_state']['hotel']"
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"dst.update([['Inform', 'Hotel', 'Area', 'north']])\n",
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"dst.state['belief_state']['hotel']"
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"dst.update([['Request', 'Hotel', 'Area', '?']])\n",
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"dst.state['request_state']"
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"dst.update([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])\n",
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"dst.state['belief_state']['hotel']"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"dst.state"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Taktyka Prowadzenia Dialogu\n",
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"\n",
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"Prosta taktyka prowadzenia dialogu dla systemu rezerwacji pokoi hotelowych mo\u017ce sk\u0142ada\u0107 si\u0119 z nast\u0119puj\u0105cych regu\u0142:\n",
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"\n",
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" 1. Je\u017celi u\u017cytkownik przekaza\u0142 w ostatniej turze akt typu `Request`, to udziel odpowiedzi na jego\n",
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" pytanie.\n",
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"\n",
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" 2. Je\u017celi u\u017cytkownik przekaza\u0142 w ostatniej turze akt typu `Inform`, to zaproponuj mu hotel\n",
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" spe\u0142niaj\u0105cy zdefiniowane przez niego kryteria.\n",
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"\n",
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" 3. Je\u017celi u\u017cytkownik przekaza\u0142 w ostatniej turze akt typu `Inform` zawieraj\u0105cy szczeg\u00f3\u0142y\n",
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" rezerwacji, to zarezerwuj pok\u00f3j.\n",
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"\n",
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"Metoda `predict` taktyki `SimpleRulePolicy` realizuje regu\u0142y przedstawione powy\u017cej."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"import copy\n",
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"import json\n",
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"from copy import deepcopy\n",
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"\n",
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"from convlab2.policy.policy import Policy\n",
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"from convlab2.util.multiwoz.dbquery import Database\n",
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"from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA, REF_USR_DA\n",
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"\n",
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"\n",
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"class SimpleRulePolicy(Policy):\n",
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" def __init__(self):\n",
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" Policy.__init__(self)\n",
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" self.db = Database()\n",
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"\n",
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" def predict(self, state):\n",
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" self.results = []\n",
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" system_action = defaultdict(list)\n",
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" user_action = defaultdict(list)\n",
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"\n",
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" for intent, domain, slot, value in state['user_action']:\n",
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" user_action[(domain, intent)].append((slot, value))\n",
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"\n",
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" for user_act in user_action:\n",
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" self.update_system_action(user_act, user_action, state, system_action)\n",
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"\n",
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" # Regu\u0142a 3\n",
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" if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['Stay', 'Day', 'People']):\n",
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" if self.results:\n",
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" system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n",
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"\n",
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" system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]\n",
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" state['system_action'] = system_acts\n",
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" return system_acts\n",
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"\n",
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" def update_system_action(self, user_act, user_action, state, system_action):\n",
|
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" domain, intent = user_act\n",
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" constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()]['semi'].items() if value != '']\n",
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" self.results = deepcopy(self.db.query(domain.lower(), constraints))\n",
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"\n",
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" # Regu\u0142a 1\n",
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" if intent == 'Request':\n",
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" if len(self.results) == 0:\n",
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" system_action[(domain, 'NoOffer')] = []\n",
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" else:\n",
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" for slot in user_action[user_act]:\n",
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" kb_slot_name = REF_SYS_DA[domain].get(slot[0], slot[0])\n",
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"\n",
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" if kb_slot_name in self.results[0]:\n",
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" system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(kb_slot_name, 'unknown')])\n",
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"\n",
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" # Regu\u0142a 2\n",
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" elif intent == 'Inform':\n",
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" if len(self.results) == 0:\n",
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" system_action[(domain, 'NoOffer')] = []\n",
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" else:\n",
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" system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
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" choice = self.results[0]\n",
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"\n",
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" if domain in [\"Hotel\", \"Attraction\", \"Police\", \"Restaurant\"]:\n",
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" system_action[(domain, 'Recommend')].append(['Name', choice['name']])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Podobnie jak w przypadku akt\u00f3w u\u017cytkownika akty systemowe przekazywane do modu\u0142u NLG s\u0105 czteroelementowymi listami z\u0142o\u017conymi z:\n",
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"\n",
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" - nazwy aktu systemowe,\n",
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" - nazwy dziedziny, kt\u00f3rej dotyczy wypowied\u017a,\n",
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" - nazwy slotu,\n",
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" - warto\u015bci slotu.\n",
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"\n",
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"Sprawd\u017amy jakie akty systemowe zwraca taktyka `SimpleRulePolicy` w odpowiedzi na zmieniaj\u0105cy si\u0119 stan dialogu."
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"from convlab2.dialog_agent import PipelineAgent\n",
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"dst.init_session()\n",
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"policy = SimpleRulePolicy()\n",
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"agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
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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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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
|
|
"source": [
|
|
"agent.response([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"lines_to_next_cell": 0
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response([['Inform', 'Hotel', 'Area', 'north']])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"lines_to_next_cell": 0
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response([['Request', 'Hotel', 'Area', '?']])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Testy End-to-End\n",
|
|
"\n",
|
|
"Na koniec przeprowad\u017amy dialog \u0142\u0105cz\u0105c w potok nasze modu\u0142y\n",
|
|
"z modu\u0142ami NLU i NLG dost\u0119pnymi dla MultiWOZ w \u015brodowisku `ConvLab-2`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from convlab2.nlu.svm.multiwoz import SVMNLU\n",
|
|
"from convlab2.nlg.template.multiwoz import TemplateNLG\n",
|
|
"\n",
|
|
"nlu = SVMNLU()\n",
|
|
"nlg = TemplateNLG(is_user=False)\n",
|
|
"agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"lines_to_next_cell": 0
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response(\"I need a cheap hotel with free parking .\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"lines_to_next_cell": 0
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response(\"Where it is located ?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"lines_to_next_cell": 0
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response(\"I would prefer the hotel be in the north part of town .\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"agent.response(\"Yeah , could you book me a room for 2 people for 4 nights starting Tuesday ?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Zauwa\u017cmy, ze nasza prosta taktyka dialogowa zawiera wiele luk, do kt\u00f3rych nale\u017c\u0105 m.in.:\n",
|
|
"\n",
|
|
" 1. Niezdolno\u015b\u0107 do udzielenia odpowiedzi na przywitanie, pro\u015bb\u0119 o pomoc lub restart.\n",
|
|
"\n",
|
|
" 2. Brak regu\u0142 dopytuj\u0105cych u\u017cytkownika o szczeg\u00f3\u0142y niezb\u0119dne do dokonania rezerwacji takie, jak d\u0142ugo\u015b\u0107 pobytu czy liczba os\u00f3b.\n",
|
|
"\n",
|
|
"Bardziej zaawansowane modu\u0142y zarz\u0105dzania dialogiem zbudowane z wykorzystaniem regu\u0142 mo\u017cna znale\u017a\u0107 w\n",
|
|
"\u015brodowisku `ConvLab-2`. Nale\u017c\u0105 do nich m.in. monitor [RuleDST](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/dst/rule/multiwoz/dst.py) oraz taktyka [RuleBasedMultiwozBot](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/rule/multiwoz/rule_based_multiwoz_bot.py).\n",
|
|
"\n",
|
|
"Zadania\n",
|
|
"-------\n",
|
|
" 1. Zaimplementowa\u0107 w projekcie monitor stanu dialogu.\n",
|
|
"\n",
|
|
" 2. Zaimplementowa\u0107 w projekcie taktyk\u0119 prowadzenia dialogu.\n",
|
|
"\n",
|
|
"Termin: 24.05.2021, godz. 23:59.\n",
|
|
"\n",
|
|
"Literatura\n",
|
|
"----------\n",
|
|
" 1. Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, I\u00f1igo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gasic, MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. EMNLP 2018, pp. 5016-5026\n",
|
|
" 2. Cathy Pearl, Basic principles for designing voice user interfaces, https://www.oreilly.com/content/basic-principles-for-designing-voice-user-interfaces/ data dost\u0119pu: 21 marca 2021\n",
|
|
" 3. Cathy Pearl, Designing Voice User Interfaces, Excerpts from Chapter 5: Advanced Voice User Interface Design, https://www.uxmatters.com/mt/archives/2018/01/designing-voice-user-interfaces.php data dost\u0119pu: 21 marca 2021"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"jupytext": {
|
|
"cell_metadata_filter": "-all",
|
|
"main_language": "python",
|
|
"notebook_metadata_filter": "-all"
|
|
},
|
|
"author": "Marek Kubis",
|
|
"email": "mkubis@amu.edu.pl",
|
|
"lang": "pl",
|
|
"subtitle": "9.Zarz\u0105dzanie dialogiem z wykorzystaniem regu\u0142[laboratoria]",
|
|
"title": "Systemy Dialogowe",
|
|
"year": "2021"
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
} |