Lingvist - Statistical Methods in Language Learning

LINGVIST | Language learning meets AI
STATISTICAL METHODS IN
LANGUAGE LEARNING
Machine Learning Estonia Meetup
2017-02-28

LINGVIST | Language learning meets AI lingvist.com@lingvist
Last time…
“We have very little Machine Learning”
- paraphrasing Ahti

Lets fix it!

At the same time in Marketing team…

Lingvist Intro
• Foreign language learning application
• We are obsessed with learning speed
• Currently free to use
• Web, iOS, Android versions
• 16 courses (language pairs) publicly available
ET-EN, ET-FR,
RU-EN, RU-FR,
EN-DE, EN-ES, EN-FR, EN-RU,
AR-EN, DE-EN, FR-EN, ES-EN, JA-EN, PT-EN, ZH-Hant-EN, ZH-Hans-EN
Homepage: lingvist.com

LINGVIST | Language learning meets AI @lingvist lingvist.com

You are expected to
type in the correct
answer

If you don’t know
then we show
correct answer

Well done!

We use statistics to…
• Prepare the course material
• Predict what learners already know
• Choose optimal repetition intervals during learning
• Analyze common mistakes learners do (and help them to avoid these)
We use conversion, retention,
engagement statistics also to drive most
product decisions but I will not talk
about it today.

Course material preparation

Frequency based vocabulary
Objective:
• Teach vocabulary based on frequency
• Quickly reach to level which is practically useful
• French: ~2000 words covers ~80% words in typical text
Solution:
• Acquire big text corpus
• Parse and tag (noun, verb, …) all words
• Build word list in frequency order
• Adjust ranking (down-rank pronouns, articles, …)
• Review and adjustments by linguists

Sample sentence extraction
Objective:
• Sentences should represent typical context
• Manual production is very time consuming
Solution:
• Extract candidate sentence/phrases from text corpus
• Rank sentences based on set of criteria
• Linguists choose the most suitable
• Sentences are redacted for consistency and completeness

Sample sentence ranking
Ranking criteria:
• C1. Sentence length
• C2. Complete sentence
• C3. Previously learned words in course
• C4. Natural sequence of words ("fast car“ vs “brave car”)
• C5. Contain relevant context words (“go home”)
• C6. Thematically consistent (“flower” and “bloom”)
Total score is weighted sum of sub-scores.

Extracted sample sentences sample

Dr. Haystack
• English corpus size used was ~3.7bln words
• There is no conversational corpora of required size
• Number of criteria leads to “The curse of dimensionality”
• Words rarely used in context that linguists consider as good example
• Harder than needle in the haystack

Predicting what user already knows

Objective:
• We have many users with previous knowledge in language
• If we could predict what they know already...
- then we can exclude these words
- save time
- avoid boredom
• We have placement test feature for about a year
- prediction is based on word frequencies
- but this correlation is not high and we miss many known words
- it still has a big positive impact on user retention
- can we do better?

User wait doubt letter between son wait Target word: wonder
User 1 1 1 1 0 1 0 0
User 2 1 0 1 0 1 1 1
User 3 0 0 0 1 1 1 1
How?
• We don't teach new words – we ask first
• What person already knows is valuable information
Training the models:
• Take all first answers from learning history (correct answer = user knows the word already)
• Train model per word to predict knowledge of that word
• Rank words by their predictive power
• Train second model for each word using fixed set of most predictive words as inputs

• 5000 models for each course (one model for each word in course)
• User answers most predictive words (up to 50 words)
• For each word in the course feed answers as input
• Get the prediction for each word
• Include or exclude word in course based on prediction
• Include small % of excluded words despite (for validation)

Averages of performance metrics:
RU-EN course Random Forest
first 4000 words
Random Forest
first 2000 words
Accuracy 0.74 0.72
Precision for “known” 0.67 0.72
Recall for “known” 0.69 0.72
Precision for “unknown” 0.52 0.52
Recall for “unknown” 0.54 0.57
Training samples 2440 4959

Challenges:
• Distribution of samples is heavily skewed to beginning of the course
• Dataset is biased due current placement test implementation:
- we excluded word if we predicted user knows the word
- so we have little data about true positives and false positives
• Model has worse performance for some language pairs
• Order of the words in the course influences the model

Predicting optimal repetition interval

Based on :
• Forgetting curve: exponential decay, Hermann Ebbinghaus ~1885
• Spaced repetition: C.A.Mace ~1932
Forgetting curve parameters are:
• highly individual (depends on person)
• highly contextual (depends on fact what is learned)
Challenge:
Measure or estimate forgetting curve parameters
• for this particular person
• for this particular word or skill

Objective:
• Target word with learning history (3x, 1/10/50min, wrong/correct/wrong)
• Predict interval user answering correctly with desired probability (~80-90%)
Method:
• Take user learning history (all answers and preceding histories)
• Calculate distance to our target word
• Choose up to ~100 learning histories most similar to target word
• Fit the curve through next repetition intervals and answers
• Calculate the interval for desired probability that user answers correctly

Word # answers Last
interval Last correct + N parameters Next interval Next correct
voiture 3 50 min Yes … ??? 80-90%
reste 2 6 min No 4 min Yes
reste 3 4 min Yes 1 hr Yes
voyage 3 30 min Yes 3 hrs No
voyage 4 3 hrs No 2 hrs Yes
… …
devriez 12 2 wk Yes 10 wk No
Clustering similar histories

Curve fitting

Mistake classification

Mistake classification
• Extract all wrong answers
• Classify wrong answers: typos, wrong grammar form, synonyms, false-friends, …
• Sort by most common mistakes
• … and figure out what we can do about it

Reducing mistakes
• Improve the sample sentence
• Give hints to user
• Allow use to try-again

Concluding remarks

Some learnings
• Deterministic history leads to biases
• Adding some randomizations is good for discovery
• Each language pair is analyzed separately (RU-EN vs FR-EN)
• Noise (typos, bad samples etc) must be accounted for

Technology
• Python (3.x)
• NumPy, Scipy, Pandas – statistics, clustering, calculations
• Scikit-Learn - machine mearning (Random Forest, Multinominal Bayes, feature extraction)
• Gensim – distributional semantics (CBOW, word2vec, skip-gram …)
• Semspaces – functions for working with semantic spaces
• NLTK, Freeling, Stanford NLP – parsing, PoS tagging, pre-processing

THANK YOU!
Credits go to team, mistakes are mine!

Lingvist - Statistical Methods in Language Learning

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