Question Generator
The Question Generator takes a Document as input and generates questions which it believes can be answered by the Document.
This is almost the inverse of the Reader which takes a question and Documents as input and returns an Answer.
QuestionGenerator models can be trained using Question Answering datasets.
| Position in a Pipeline | At the very beginning of a querying Pipeline |
| Input | Documents |
| Output | Documents |
| Classes | QuestionGenerator |
Disambiguation: The QuestionGenerator is different to the Generator.
The QuestionGenerator receives only documents as input and returns questions as output
while the Generator class is an alternative to the Reader.
It takes a question and documents as input and returns an answer.
Usage
To run a stand-alone Question Generator:
from haystack.nodes import QuestionGenerator
text = """Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossumand first released in 1991, Python's design philosophy emphasizes codereadability with its notable use of significant whitespace."""
qg = QuestionGenerator()result = qg.generate(text)
The output will look like this:
[' Who created Python?', ' When was Python first released?', " What is Python's design philosophy?"]
Ready-Made Pipelines
In Haystack, there are 2 pipeline configurations that are already encapsulated in its own class.
QuestionGenerationPipelineQuestionAnswerGenerationPipeline
Have a look at our ready-made pipelines page to learn more about them. Check out the question generation tutorial to start using them.
Use Case: Auto-Suggested Questions
Generated questions can help users get closer to the information that they are looking for.
Search engines now present auto-suggested questions to your top search results and even present suggested answers.
It is possible to build this same functionality in Haystack using the QuestionGenerator.
After your Retriever has returned some candidate documents, you can run the QuestionGenerator to suggest more answerable questions.
By presenting these generated questions to your users, you can give them a sense of other facts and topics that are present in the documents.
You can go even on step further by predicting answers to these questions with a Reader or Generator.
Use Case: Human in the Loop Annotation
A QuestionGenerator can enable different annotation workflows.
For example, given a text corpus, you could use the QuestionGenerator to create questions,
but you can also use then use a Reader to predict answers.
Correct QA pairs created in this manner might not be so effective in retraining your Reader model.
However, correcting wrong QA pairs creates training samples that your model found challenging.
These examples are likely to be impactful when it comes to retraining.
This is also a quicker workflow than having annotators generate both question and answer.