This project was originally intended for an AI course at Sofia University. During it's execution, I was constraint on time and couldn't implement all the ideas I had, but I plan to continue working on it.
The idea is to generate multiple choice answers from text, by splitting this complex problem to simpler steps:
- Identify keywords from the text and use them as answers to the questions.
- Replace the answer from the sentence with blank space and use it as the base for the question.
- Transform the sentence with a blank space for answer to a more question-like sentence.
- Generate distractors, words that are similar to the answer, as incorrect answers.
To avoid any conflicts with python packages from other projects, it is a good practice to create a virtual environment in which the packages will be installed. If you do not want to this you can skip the next commands and directly install the the requirements.txt file.
Create a virtual environment :
python -m venv venv
Enter the virtual environment:
Windows:
. .\venv\Scripts\activate
Linux or MacOS
source .\venv\Scripts\activate
Install ipython inside the venv:
ipython kernel install --user --name=.venv
Install jupyter lab inside the venv:
pip install jupyterlab
pip install -r .\requirements.txt
jupyter lab
Before I could to anything, I wanted to understand more about how questions are made and what kind of words are it's answers.
I used the SQuAD 1.0 dataset which has about 100 000 questions generated from Wikipedia articles.
You can read about the insights I've found in the Data Exploration jupyter notebook.
My assumption was that words from the text would be great answers for questions. All I needed to do was to decide which words, or short phrases, are good enough to become answers.
I decided to do a binary classification on each word from the text. spaCy really helped me with the word tagging.
I pretty much needed to create the entire dataset for the binary classification. I extracted each non-stop word from the paragraphs of each question in the SQuAD dataset and added some features on it like:
- Part of speech
- Is it a Named entity
- Are only alpha characters used
- Shape - whether it's only alpha characters, digits, has punctuation (xxxx, dddd, Xxx X. Xxxx)
- Word count
And the label isAnswer - whether the word extracted from the paragraph is the same and in the same place as the answer of the SQuAD question.
Some other features like TF-IDF score and cosine similarity to the title would be the great, but I didn't have the time to add them.
Other than those, it's up to our imagination to create new features - maybe whether it's in the start, middle or end of a sentence, information about the words surrounding it and more... Though before adding more feature it would be nice to have a metric to assess whether the feature is going to be useful or not.
I found the problem similar to spam filtering, where a common approach is to tag each word of an email as coming from a spam or not a spam email.
I used scikit-learn's Gaussian Naive Bayes algorithm to classify each word whether it's an answer.
The results were surprisingly good - at a quick glance, the algorithm classified most of the words as answers. The ones it didn't were in fact unfit.
The cool thing about Naive Bayes is that you get the probability for each word. In the demo I've used that to order the words from the most likely answer to the least likely.
Another assumption I had was that the sentence of an answer could easily be turned to a question. Just by placing a blank space in the position of the answer in the text I get a "cloze" question (sentence with a blank space for the missing word)
Answer: Oxygen
Question: _____ is a chemical element with symbol O and atomic number 8.
I decided it wasn't worth it to transform the cloze question to a more question-looking sentence, but I imagine it could be done with a seq2seq neural network, similarly to the way text is translated from one language to another.
The part turned out really well.
For each answer I generate it's most similar words using word embeddings and cosine similarity.
Most of the words are just fine and could easily be mistaken for the correct answer. But there are some which are obviously not appropriate.
Since I didn't have a dataset with incorrect answers I fell back on a more classical approach.
I removed the words that weren't the same part of speech or the same named entity as the answer, and added some more context from the question.
I would like to find a dataset with multiple choice answers and see if I can create a ML model for generating better incorrect answers.
After adding a Demo project, the generated questions aren't really fit to go into a classroom instantly, but they are't bad either.
The cool thing is the simplicity and modularity of the approach, where you could find where it's doing bad (say it's classifying verbs) and plug a fix into it.
Having a complex Neural Network (like all the papers on the topics do) will probably do better, especially in the age we're living. But the great thing I found out about this approach, is that it's like a gateway for a software engineer, with his software engineering mindset, to get into the field of AI and see meaningful results.
I find this topic quite interesting and with a lot of potential. I would probably continue working in this field.
I even enrolled in a Masters of Data Mining and will probably do some similar projects. I will link anything useful here.
I've already put some more time on finishing the project, but I would like to transform it more to a tutorial about getting into the field of AI while having the ability to easily extend it with new custom features.
Update - 29.12.19: The repository has become pretty popular, so I added a new notebook (Demo.ipynb) that combines all the modules and generates questions for any text. I reordered the other notebooks and documented the code (a bit better).
Update - 09.03.21: Added a requirements.txt file with instructions to run a virtual environment and fixed the bug a with ValueError: operands could not be broadcast together with shapes (230, 121) (83, )
I have also started working on my Master's thesis with a similar topic of Question Generation. If you are interested in the field or looking into improving upon this repo, you can check this great article by Adam Montgomerie or this repo by Patil Suraj where they are using transformers which seems to be the current trend in NLP.