For example, here is a cloze test from the previously linked Wikipedia article.
Today, I went to the ________ and bought some milk and eggs. I knew it was going to rain, but I forgot to take my ________, and ended up getting wet on the way.
The interesting thing about cloze tests is that they force you to understand both vocabulary and the context where it is used. In the previous example, the first blank is preceded by the word the, and therefore must be followed by a noun, adjective, or adverb. However, a conjunction follows the blank; the sentence would not be grammatically correct if anything other than a noun were in the blank. Correctly completing the test forces the learner to understand both vocabulary and grammatical rules of vocabulary usage. This makes cloze tests ideal for learning and testing a second language.
I use
Anki — a program that helps make remembering things as easy as possible — to automate how frequently I complete cloze tests. Because Anki is a lot more efficient than traditional study methods, you can greatly decrease your time spent studying to learn vocabulary. Unfortunately, Anki requires you to create your own cloze tests, which can be a tedious and time consuming process. Being a software engineer, I began looking at how to automate the process of creating cloze tests for Anki and came up with an automatic method using publicly available data.
The rest of this article describes the process I used for automatically creating cloze tests for import into Anki, followed by links to download pre-packaged Anki decks for learning French as an English speaker created using this method..
Raw Sentences
The first step is to collect a lot of sentences in your target language. Ideally, sentences would have a translation in your native language to help understand any contextual clues for completing the cloze test. The website
Tatoeba provides a collection of user generated sentences and translations, which makes an ideal data set for sentences. All data is available for download under a Creative Commons Attribution 2.0 license (CC-BY 2.0) as a set of simple tab separated files. The files come in two formats: first, a list of all sentences and the language they are written in, and second, a list of links between the sentences that shows which sentences are translations of each other.
The sentences dataset looks like the following, where the first number is the sentence identifier, followed by the language, followed by the text.
107 deu Ich kann mich nur fragen, ob es für alle anderen dasselbe ist.
1115 fra Lorsqu'il a demandé qui avait cassé la fenêtre, tous les garçons ont pris un air innocent.
6557647 eng I sing of arms and the man, made a fugitive by fate, who first came from the coasts of Troy, to Italy and the Lavinian shores.
The links dataset provides a simple mapping between sentence numbers, showing which sentences are translations of each other.
1 77
1 1276
1 2481
1 5350
1 5972
1 180624
1 344899
1 345549
1 380381
1 387119
Frequency Lists
The sentences from Tatoeba provide a great starting point for generating cloze deletions. The question still remains, which word to use for the cloze? That is, which word will be the blank in the fill in the blank problem?
Not all words are created equal, some are so common that they are used in almost every sentence and are therefore not that interesting to learn, others are used so infrequently that they would not be useful for everyday speech. In the middle are the 20,000 or so words that are used regularly enough that they can provide a basis for fluency in a second language.
A sorting of words into the frequency with which they appear in a corpus of language is called a
frequency list.
Wiktionary provides a list of frequency lists available in many languages. I used a premade list made available under an MIT license
on Github. This list tracks the 50,000 most frequently used words in TV and movie subtitles maintained by the
OpenSubtitles project.
I chose the cloze deletion to test using the same method used by
clozemaster.
The cloze deletion to test, or the blank in the sentence, is the least common word in the sentence within the [5]0,000 … most common words in the language. In other words, for a given sentence all the words in the sentence are checked against the top [5]0,000 words in a fequency list for that language. The least common word is then used as the cloze test. In this way the vocab learned via clozemaster is the most difficult of the most common.
Generating the Cloze Tests
Using the original sentences with translations, and a frequency list, I created a set of cloze deletions using a Python script.
Finding target and native language sentences
The first step was to extract out the sentences from Tatoeba that are available in my native language and target language. I used a simple grep expression to create two files from the original sentence data.
grep -E '\teng\t' sentences.csv > native_sentences.csv
grep -E '\tfra\t' sentences.csv > target_sentences.csv
Choosing the cloze word
The following function takes a sentence and a frequency list (as a map), and chooses a cloze word. I skipped any words that were capitalized and removed any words that were two characters or shorter. Any words left over were checked against the frequency list and the minimum frequency word was used for the cloze. If there were no words in the sentence that had frequency data attached, a random word was chosen.
def find_cloze(sentence, frequency_list):
"""
Return the least frequently used word in the sentence by.
If no word is found in the frequency list, return a random word.
If no acceptable word is available (even random), return None
"""
# Remove punctuation
translator = str.maketrans(string.punctuation, ' '*len(string.punctuation))
sentence = sentence.translate(translator)
max_frequency = 50001 # Frequency list has 50,000 entries
min_frequency = max_frequency
min_word = None
valid_words = []
for word in sentence.split():
if word.isupper() or word.istitle():
continue # Skip proper nouns
if len(word) <= 2:
continue # Skip tiny words
valid_words.append(word)
word_frequency = int(frequency_list.get(word.lower(), max_frequency))
if word_frequency < min_frequency:
min_word = word
min_frequency = word_frequency
if min_word:
return min_word
else:
if valid_words:
return random.choice(valid_words)
else:
return None
Synthesizing speech
I used
Amazon Polly to attach audio samples to the target language. The script randomly chooses a voice in the target language to add a bit of variety to the audio.
import boto3
polly = boto3.client('polly')
def synthesize_speech(text, filename):
"""
Synthesize speech using Amazon polly
"""
voices = ['Celine', 'Mathieu', 'Chantal']
voice = random.choice(voices)
response = polly.synthesize_speech(
OutputFormat='mp3',
Text=text,
VoiceId=voice
)
output = os.path.join("./out/", filename)
if "AudioStream" in response:
with closing(response["AudioStream"]) as stream:
with open(output, "wb") as outfile:
outfile.write(stream.read())
Generating a CSV for Anki
Given the algorithm for choosing a cloze word, and a method for synthesizing speech, the last step is to generate a CSV file from the data sets that can be input into Anki. The CSV generation requires a file of french and english sentences, the links file between the translations, and the frequency list. It matches sentences with their translations, chooses a cloze deletion, and synthesizes an audio sample of the sentence.
def make_index(path, delimiter, value=1):
"""
Given a CSV reader, return a map between the first column and the
column specified by value.
"""
d = dict()
with open(path, newline='') as f:
reader = csv.reader(f, delimiter=delimiter)
for row in reader:
d[row[0]] = row[value]
return d
def generate(french_sentence_file,
english_sentence_file,
links_file,
frequency_list_file):
# Make index between sentence number and rest of csv
print("Making indexes ...")
french = make_index(french_sentence_file, '\t', value=2)
english = make_index(english_sentence_file, '\t', value=2)
links = make_index(links_file, '\t')
# Make index between word and usage frequency
frequency = make_index(frequency_list_file, ' ')
print("Generating clozes ...")
with open("out.csv", 'w', newline='') as outfile:
writer = csv.writer(outfile, delimiter='\t',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
# For each French sentence
for fra_number, fra_sentence in french.items():
# Lookup English translation
eng_number = links.get(fra_number)
if not eng_number:
continue # If no English translation, skip
eng_sentence = english.get(eng_number)
if not eng_sentence:
continue # If no English translation, skip
# Find the cloze word
fra_cloze_word = find_cloze(fra_sentence, frequency)
if not fra_cloze_word:
continue # If no cloze word, skip
clozed = fra_sentence.replace(fra_cloze_word,
'{{{{c1::{}}}}}'.format(fra_cloze_word)
# Generate audio
audio_filename = 'fra-{}-audio.mp3'.format(fra_number)
if not os.path.isfile(audio_filename):
synthesize_speech(fra_sentence, audio_filename)
writer.writerow([fra_number,
clozed,
eng_number,
eng_sentence,
'[sound:{}]'.format(audio_filename)])
print("Done.")
Importing into Anki
Given the CSV file and a list of mp3 audio samples, the next step is to import the data into Anki. The audio is imported by copying the files into Anki’s
collection.media folder. See the
Anki manual for more information.
Once the media files are imported, you can import the CSV file generated by the cloze script into Anki. To import a file, click the File menu and then “Import”. For more information, see the
Anki manual.
Downloads
I used this process to generate French cloze deletions suitable for English speakers learning French. You can download pre-made packages that you can import into Anki to start learning French
here.
You might sometimes need a quick transcription of some audio notes and you might not feel like rewinding back and forth. Luckily, there is a very easy way to do it using YouTube’s auto-captioning system.
LIKEsearches? Your post is very, very unclear... – ılǝ Oct 9 '13 at 7:21how to make a list parallel sentences that a particular word exist- your query is correct. Sorry - it remains unclear if you are asking how to optimize the speed of the query, or how to do a join on the tables, or how to deal with special characters or, or, or – ılǝ Oct 9 '13 at 7:41