KWT-2024/lab/lab_08.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "improved-register",
   "metadata": {},
   "source": [
    "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Komputerowe wspomaganie tłumaczenia </h1>\n",
    "<h2> 8. <i>Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia</i> [laboratoria]</h2> \n",
    "<h3>Rafał Jaworski (2021)</h3>\n",
    "</div>\n",
    "\n",
    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hungarian-davis",
   "metadata": {},
   "source": [
    "W dzisiejszych czasach, niezwykle ważną techniką wspomagania tłumaczenia jest użycie tłumaczenia maszynowego. Tekst źródłowy do tłumaczenia jest najpierw tłumaczony całkowicie autommatycznie, a następnie tłumacz ludzki dokonuje korekty wyniku. Technologia tłumaczenia maszynowego jest już na tyle dojrzała, że oferuje bardzo wysoką jakość wyników. Coraz częstsze stają się scenariusze, w których ludzka korekta to niemal całkowicie machinalne (sic!) zatwierdzanie wyników tłumaczenia maszynowego. Na dzisiejszych zajęciach poznamy techniki ewaluacji tłumaczenia maszynowego oraz sprawdzania jego przydatności w procesie wspomagania tłumaczenia ludzkiego."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-commons",
   "metadata": {},
   "source": [
    "Jakość tłumaczenia maszynowego możemy oceniać na dwóch niezależnych płaszczyznach: dokładność i płynność. Płynność jest subiektywnie odbieranym odczuciem, że czytany tekst jest napisany językiem naturalnym i zrozumiałym. Systemy tłumaczenia maszynowego oparte na uczeniu głębokim z wykorzystaniem sieci neuronowych osiągają duży stopień płynności tłumaczenia. Niestety jednak ich dokładność nie zawsze jest równie wysoka."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "referenced-implement",
   "metadata": {},
   "source": [
    "Dokładność tłumaczenia maszynowego jest parametrem, który łatwiej zmierzyć. Wartość takich pomiarów daje obraz tego, jaka jest faktyczna jakość tłumaczenia maszynowego i jaka jest jego potencjalna przydatność we wspomaganiu tłumaczenia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disturbed-january",
   "metadata": {},
   "source": [
    "Najczęściej stosowaną techniką oceny tłumaczenia maszynowego jest ocena BLEU. Do obliczenia tej oceny potrzebny jest wynik tłumaczenia maszynowego oraz referencyjne tłumaczenie ludzkie wysokiej jakości."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dental-combination",
   "metadata": {},
   "source": [
    "### Ćwiczenie 1: Zaimplementuj program do obliczania oceny BLEU dla korpusu w folderze data. Użyj implementacji BLEU z pakietu nltk. Dodatkowe wymaganie techniczne - napisz program tak, aby nie musiał rozpakwowywać pliku zip z korpusem na dysku."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4f068df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.984587822441638e-156"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import zipfile\n",
    "import nltk.translate.bleu_score as bleu\n",
    "import string\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    text_without_punctuations = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    sentences = text_without_punctuations.split('\\n')\n",
    "    return [[word.lower() for word in sentence.split()] for sentence in sentences if sentence != '']\n",
    "\n",
    "def calculate_bleu():\n",
    "    zip = zipfile.ZipFile('data/corpus_corrected.zip')\n",
    "    files = {name: remove_punctuation(zip.read(name).decode('utf-8'))\n",
    "            for name in zip.namelist()}\n",
    "        \n",
    "    corpus_de_human, corpus_de_nmt = files['corpus_de_human.txt'], files['corpus_de_nmt.txt']\n",
    "    \n",
    "    return bleu.corpus_bleu(corpus_de_human, corpus_de_nmt)\n",
    "\n",
    "calculate_bleu()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-ethics",
   "metadata": {},
   "source": [
    "### Ćwiczenie 2: Oblicz wartość bleu na różnych fragmentach przykładowego korpusu (np. na pierwszych 100 zdaniach, zdaniach 500-600). Czy w jakimś fragmencie korpusu jakość tłumaczenia znacząco odbiega od średniej?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "lasting-rolling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 to 100 - 4.97555004481153e-232\n",
      "500 to 600 - 5.956707985683837e-232\n",
      "800 to 900 - 4.774461089627919e-232\n",
      "200 to 300 - 5.56331772444502e-232\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def analyze_bleu(start_sentence_index, finish_sentence_index):\n",
    "    zip = zipfile.ZipFile('data/corpus_corrected.zip')\n",
    "    files = {name: remove_punctuation(zip.read(name).decode('utf-8'))\n",
    "            for name in zip.namelist()}\n",
    "        \n",
    "    corpus_de_human, corpus_de_nmt = files['corpus_de_human.txt'][start_sentence_index:finish_sentence_index], files['corpus_de_nmt.txt'][start_sentence_index:finish_sentence_index]\n",
    "    \n",
    "    return bleu.corpus_bleu(corpus_de_human, corpus_de_nmt)\n",
    "\n",
    "\n",
    "print(\"0 to 100 - \"+str(analyze_bleu(0, 100)))\n",
    "print(\"500 to 600 - \"+str(analyze_bleu(500, 600)))\n",
    "print(\"800 to 900 - \"+str(analyze_bleu(800, 900)))\n",
    "print(\"200 to 300 - \"+str(analyze_bleu(200, 300)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-bikini",
   "metadata": {},
   "source": [
    "Inną metodą oceny jakości tłumaczenia maszynowego jest parametr WER - Word Error Rate. Definiuje się on w następujący sposób:\n",
    "\n",
    "$WER = \\frac{S+D+I}{N}=\\frac{S+D+I}{S+D+C}$\n",
    "\n",
    "gdzie:\n",
    " * S - liczba substytucji (słów)\n",
    " * D - liczba usunięć\n",
    " * I - liczba wstawień\n",
    " * C - liczba poprawnych śłów\n",
    " * N - liczba słów w tłumaczeniu referencyjnym (N=S+D+C)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4f02ae",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "conscious-cookbook",
   "metadata": {},
   "source": [
    "Miara ta jest zwykle używana w do oceny systemów automatycznego rozpoznawania mowy, jednak w kontekście wspomagania tłumaczenia może być rozumiana jako wielkość nakładu pracy tłumacza nad poprawieniem tłumaczenia maszynowego."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "split-palace",
   "metadata": {},
   "source": [
    "### Ćwiczenie 3: Oblicz wartość WER dla przykładowego korpusu. Skorzystaj z gotowej implementacji WER."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "occupied-swing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17355216569308377"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jiwer import wer\n",
    "import zipfile\n",
    "\n",
    "def calculate_wer():\n",
    "    ourZip = zipfile.ZipFile('data/corpus_corrected.zip')\n",
    "    files = {name: remove_punctuation(ourZip.read(name).decode('utf-8'))\n",
    "            for name in ourZip.namelist()}\n",
    "        \n",
    "    corpus_de_human, corpus_de_nmt = files['corpus_de_human.txt'], files['corpus_de_nmt.txt']\n",
    "\n",
    "    sum_wer = 0\n",
    "    for human_sent, nmt_sent in zip(corpus_de_human, corpus_de_nmt):\n",
    "        sum_wer+= wer(\" \".join(human_sent), \" \".join(nmt_sent))\n",
    "\n",
    "    return sum_wer/(len(corpus_de_human))\n",
    "\n",
    "calculate_wer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stretch-wound",
   "metadata": {},
   "source": [
    "Poza wymienionymi powyżej, stosować można jeszcze inne miary oparte na porównywaniu tłumaczenia maszynowego z ludzkim. Przypomnijmy sobie jedną, którą stosowaliśmy wcześniej."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-wilderness",
   "metadata": {},
   "source": [
    "### Ćwiczenie 4: Oblicz średnią wartość dystansu Levenshteina pomiędzy zdaniami przetłumaczonymi automatycznie oraz przez człowieka. Użyj implementacji z ćwiczeń nr 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "immediate-element",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.653"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import Levenshtein\n",
    "\n",
    "def calculate_levenshtein():\n",
    "    ourZip = zipfile.ZipFile('data/corpus_corrected.zip')\n",
    "    files = {name: remove_punctuation(ourZip.read(name).decode('utf-8'))\n",
    "            for name in ourZip.namelist()}\n",
    "        \n",
    "    corpus_de_human, corpus_de_nmt = files['corpus_de_human.txt'], files['corpus_de_nmt.txt']\n",
    "\n",
    "    sum_disatnce = 0\n",
    "    for human_element, nmt_element in zip(corpus_de_human, corpus_de_nmt):\n",
    "        sum_disatnce+=  Levenshtein.distance(human_element, nmt_element)\n",
    "\n",
    "    return sum_disatnce/(len(corpus_de_human))\n",
    "\n",
    "calculate_levenshtein()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-burton",
   "metadata": {},
   "source": [
    "A teraz sprawdźmy coś jeszcze. W danych przykładowego korpusu znajduje się także angielski tekst źródłowy. Teoretycznie, dobre tłumaczenie niemieckie powinno zawierać jak najwięcej słów z angielskiego źródła. Wykonajmy najstępujący eksperyment:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grateful-recruitment",
   "metadata": {},
   "source": [
    "### Ćwiczenie 5: Dla każdej trójki zdań z korpusu przykładowego wykonaj następujące kroki:\n",
    " * Przetłumacz każde angielskie słowo na niemiecki przy użyciu modułu PyDictionary.\n",
    " * Sprawdź, które z niemieckich tłumaczeń zawiera więcej spośród tych przetłumaczonych słów - automatyczne, czy ludzkie.\n",
    "Następnie wypisz statystyki zbiorcze. Które tłumaczenie zawiera więcej słownikowych tłumaczeń słów ze źródła?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "descending-easter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "press\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "h\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "while\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "in\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "review\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "mode\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "to\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "display\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "keyboard\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "shortcuts\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "for\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "working\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "in\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "review\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "mode\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "click\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "remove\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "shortcut\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "selects\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "nonselected\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "areas\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "for\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "more\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "information\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "see\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "screen\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "class\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "form\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "class\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "and\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "slide\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "class\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "in\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "actionscript\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "20\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "components\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "language\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "reference\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "clicking\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "mouse\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "retracts\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "bezier\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "handles\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "and\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "causes\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "curved\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "path\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "across\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "anchor\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "point\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "to\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "revert\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "to\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "straight\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "segments\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "an\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "array\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "of\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "keyvalue\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "pairs\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "that\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "represent\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "information\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "in\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "ilst\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "atom\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "which\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "is\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "equivalent\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "of\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "id3\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "tags\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "for\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "mp4\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "files\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "when\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "you\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "add\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "3d\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "comments\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "to\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "default\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "view\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "of\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "a\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "model\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "a\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "new\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "view\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "called\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "3dcommentview\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "is\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "created\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "when\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "you\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "re\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "finished\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "setting\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "options\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "click\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "set\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "default\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "set\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "location\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "for\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "stroke\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "in\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "relationship\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "to\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "the\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "marquee\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "by\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "choosing\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "inside\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "center\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "or\n",
      "Invalid Word\n",
      "None\n",
      "\n",
      "\n",
      "outside\n"
     ]
    }
   ],
   "source": [
    "from PyDictionary import PyDictionary\n",
    "import zipfile\n",
    "\n",
    "def transalate(word_list):\n",
    "     transalted_words = {}\n",
    "     for word in word_list:\n",
    "        translation = PyDictionary().translate(word, 'German')\n",
    "        if translation:\n",
    "             transalted_words[word] = translation\n",
    "\n",
    "     return transalted_words\n",
    "\n",
    "def analyze_translations():\n",
    "    ourZip = zipfile.ZipFile('data/corpus_corrected.zip')\n",
    "    files = {name: remove_punctuation(ourZip.read(name).decode('utf-8'))\n",
    "            for name in ourZip.namelist()}\n",
    "        \n",
    "    corpus_de_human, corpus_de_nmt, corpus_en = files['corpus_de_human.txt'], files['corpus_de_nmt.txt'], files['corpus_en.txt']\n",
    "\n",
    "    for human_element, nmt_element, element  in zip(corpus_de_human, corpus_de_nmt, corpus_en):\n",
    "        transalted_words = transalate(element)\n",
    "\n",
    "        words = set(re.findall(r'\\w+', sentence.lower()))\n",
    "        matches = sum(1 for word in words if translations.get(word.lower()))\n",
    "        # tranlsations = [PyDictionary().translate(word, 'de') for word in element]\n",
    "        \n",
    "\n",
    "\n",
    "analyze_translations()"
   ]
  }
 ],
 "metadata": {
  "author": "Rafał Jaworski",
  "email": "rjawor@amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "lang": "pl",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "subtitle": "8. Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia",
  "title": "Komputerowe wspomaganie tłumaczenia",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}