KWT-2024/lab/lab_08.ipynb
2024-04-23 23:52:29 +02:00

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{
"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": 37,
"id": "moving-clothing",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BLEU Score: 3.984587822441638e-156\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/potoato/.pyenv/versions/3.11.0/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",
"/Users/potoato/.pyenv/versions/3.11.0/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"
]
}
],
"source": [
"import os\n",
"import zipfile\n",
"import nltk.translate.bleu_score as bleu\n",
"import string\n",
"\n",
"def remove_punctuation(text):\n",
" no_punctuation_text = text.translate(str.maketrans('', '', string.punctuation))\n",
" sentences = no_punctuation_text.split('\\n')\n",
" return [[word.lower() for word in sentence.split()] for sentence in sentences if sentence != '']\n",
"\n",
"def extract_zip(input_zip):\n",
" input_zip = zipfile.ZipFile(input_zip)\n",
" return {name: remove_punctuation(input_zip.read(name).decode('utf-8'))\n",
" for name in input_zip.namelist()}\n",
"\n",
"def calculate_bleu(data_folder):\n",
" if not os.path.exists(data_folder):\n",
" output = extract_zip(data_folder + '.zip') # keys: 'corpus_de_human.txt', 'corpus_de_nmt.txt', 'corpus_en.txt'\n",
" human, machine = output['corpus_de_human.txt'], output['corpus_de_nmt.txt']\n",
" else:\n",
" with open(data_folder + '/corpus_de_human.txt', 'r') as file:\n",
" human = [line.strip() for line in file]\n",
" with open(data_folder + '/corpus_de_nmt.txt', 'r') as file:\n",
" machine = [line.strip() for line in file]\n",
" \n",
" return bleu.corpus_bleu(human, machine)\n",
"\n",
"print(\"BLEU Score:\", calculate_bleu('data/corpus_corrected'))"
]
},
{
"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": 90,
"id": "lasting-rolling",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/potoato/.pyenv/versions/3.11.0/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",
"/Users/potoato/.pyenv/versions/3.11.0/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",
"/Users/potoato/.pyenv/versions/3.11.0/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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average BLEU Score: 8.234832237953338e-157\n",
"Standard Deviation of BLEU Scores: 2.470449671386047e-156\n",
"Values that are outside of std: []\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"def moving_window_indexes(n, window_size, step):\n",
" return [(i, i+window_size) for i in range(0, n - window_size + 1, step)]\n",
"\n",
"def analyze_bleu(data_folder):\n",
" bleu_score = []\n",
" output = extract_zip(data_folder + '.zip')\n",
" human, machine = output['corpus_de_human.txt'], output['corpus_de_nmt.txt']\n",
" idx = moving_window_indexes(len(human), 100, 100)\n",
" for i in idx:\n",
" bleu_score.append(bleu.corpus_bleu(human[i[0]:i[1]], machine[i[0]:i[1]]))\n",
" avg_bleu = np.average(bleu_score)\n",
" std_bleu = np.std(bleu_score)\n",
" outside_bleu = [i for i, score in enumerate(bleu_score)\n",
" if score < avg_bleu - std_bleu]\n",
" return bleu_score, avg_bleu, std_bleu, outside_bleu\n",
"\n",
"bleu_score, avg_bleu, std_bleu, outside_bleu = analyze_bleu('data/corpus_corrected')\n",
"\n",
"print(\"Average BLEU Score:\", avg_bleu)\n",
"print(\"Standard Deviation of BLEU Scores:\", std_bleu)\n",
"print(\"Values that are outside of std:\", outside_bleu)"
]
},
{
"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": "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": 92,
"id": "7faff7f9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting jiwer\n",
" Downloading jiwer-3.0.3-py3-none-any.whl (21 kB)\n",
"Requirement already satisfied: click<9.0.0,>=8.1.3 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from jiwer) (8.1.7)\n",
"Requirement already satisfied: rapidfuzz<4,>=3 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from jiwer) (3.8.1)\n",
"Installing collected packages: jiwer\n",
"Successfully installed jiwer-3.0.3\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install jiwer"
]
},
{
"cell_type": "code",
"execution_count": 128,
"id": "occupied-swing",
"metadata": {},
"outputs": [],
"source": [
"from jiwer import wer\n",
"\n",
"\n",
"def remove_punctuation(text):\n",
" no_punctuation_text = text.translate(str.maketrans('', '', string.punctuation))\n",
" return no_punctuation_text\n",
"\n",
"def extract_zip(input_zip):\n",
" input_zip = zipfile.ZipFile(input_zip)\n",
" return {name: remove_punctuation(input_zip.read(name).decode('utf-8'))\n",
" for name in input_zip.namelist()}\n",
"\n",
"def calculate_wer(data_folder):\n",
" output = extract_zip(data_folder + '.zip')\n",
" human, machine = output['corpus_de_human.txt'].lower(), output['corpus_de_nmt.txt'].lower()\n",
" avg = []\n",
" for s_h, s_m in zip(human.split('\\n'), machine.split('\\n')):\n",
" if s_h == '':\n",
" continue\n",
" avg.append(wer(s_h, s_m))\n",
" return wer(human, machine), np.average(avg)\n"
]
},
{
"cell_type": "code",
"execution_count": 129,
"id": "b98eb250",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.17324274888946956, 0.1735521656930835)"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculate_wer('data/corpus_corrected')"
]
},
{
"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": 132,
"id": "immediate-element",
"metadata": {},
"outputs": [],
"source": [
"#UWAGA: poniewaz zadanie mówi o dystansie, nie bede obliczal podobienstwa levenshtein'a.\n",
"\n",
"from Levenshtein import distance as levenshtein_distance\n",
"\n",
"def calculate_levenshtein(data_folder):\n",
" output = extract_zip(data_folder + '.zip')\n",
" human, machine = output['corpus_de_human.txt'].lower(), output['corpus_de_nmt.txt'].lower()\n",
" avg = []\n",
" for s_h, s_m in zip(human.split('\\n'), machine.split('\\n')):\n",
" if s_h == '':\n",
" continue\n",
" avg.append(levenshtein_distance(s_h, s_m))\n",
" return np.average(avg)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"id": "e1eea599",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.008"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculate_levenshtein('data/corpus_corrected')"
]
},
{
"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": 135,
"id": "8ebe0ff1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting PyDictionary\n",
" Downloading PyDictionary-2.0.1-py3-none-any.whl (6.1 kB)\n",
"Collecting bs4\n",
" Downloading bs4-0.0.2-py2.py3-none-any.whl (1.2 kB)\n",
"Requirement already satisfied: click in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from PyDictionary) (8.1.7)\n",
"Collecting goslate\n",
" Downloading goslate-1.5.4.tar.gz (14 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement already satisfied: requests in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from PyDictionary) (2.31.0)\n",
"Collecting beautifulsoup4\n",
" Downloading beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)\n",
"\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.9/147.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hCollecting futures\n",
" Downloading futures-3.0.5.tar.gz (25 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /Users/potoato/.pyenv/versions/3.11.0/lib/python3.11/site-packages (from requests->PyDictionary) (3.3.2)\n",
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"Collecting soupsieve>1.2\n",
" Downloading soupsieve-2.5-py3-none-any.whl (36 kB)\n",
"Installing collected packages: futures, soupsieve, goslate, beautifulsoup4, bs4, PyDictionary\n",
"\u001b[33m DEPRECATION: futures is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559\u001b[0m\u001b[33m\n",
"\u001b[0m Running setup.py install for futures ... \u001b[?25ldone\n",
"\u001b[?25h\u001b[33m DEPRECATION: goslate is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559\u001b[0m\u001b[33m\n",
"\u001b[0m Running setup.py install for goslate ... \u001b[?25ldone\n",
"\u001b[?25hSuccessfully installed PyDictionary-2.0.1 beautifulsoup4-4.12.3 bs4-0.0.2 futures-3.0.5 goslate-1.5.4 soupsieve-2.5\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install PyDictionary"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "descending-easter",
"metadata": {},
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],
"source": [
"from PyDictionary import PyDictionary\n",
"import zipfile\n",
"import string\n",
"from tqdm import tqdm\n",
"\n",
"def remove_punctuation(text):\n",
" no_punctuation_text = text.translate(str.maketrans('', '', string.punctuation))\n",
" words = no_punctuation_text.split()\n",
" return [word.lower() for word in words if word]\n",
"\n",
"def extract_zip(input_zip):\n",
" input_zip = zipfile.ZipFile(input_zip)\n",
" return {name: input_zip.read(name).decode('utf-8')\n",
" for name in input_zip.namelist()}\n",
"\n",
"def translate_to_german(word_list):\n",
" dictionary = PyDictionary()\n",
" german_translations = []\n",
" for word in tqdm(word_list):\n",
" try:\n",
" translations = dictionary.translate(word, 'de')\n",
" german_translations.extend(translations)\n",
" except:\n",
" pass\n",
" return german_translations\n",
"\n",
"def compare_translations(data_folder):\n",
" output = extract_zip(data_folder + '.zip')\n",
" human_english = remove_punctuation(output['corpus_en.txt'])\n",
" human_german = remove_punctuation(output['corpus_de_human.txt'])\n",
" automatic_german = translate_to_german(human_english)\n",
" unique_human_german = set(human_german)\n",
" unique_automatic_german = set(automatic_german)\n",
" summary = {\n",
" \"Human Translations\": len(unique_human_german),\n",
" \"Automatic Translations\": len(unique_automatic_german),\n",
" \"Common Translations\": len(unique_human_german.intersection(unique_automatic_german))\n",
" }\n",
"\n",
" return summary\n",
"\n",
"summary = compare_translations('data/corpus_corrected')\n",
"\n",
"print(\"Summary Statistics:\")\n",
"for key, value in summary.items():\n",
" print(f\"{key}: {value}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc57f168",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.11.0"
},
"subtitle": "8. Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia",
"title": "Komputerowe wspomaganie tłumaczenia",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 5
}