| 
Answer Extraction Methods for Educational Automated Question Generation
October 01, 2020 – /,
Motivation
The thesis context is educational automatic question generation where our research group tries to automatically pose questions about arbitrary textbooks. Yet, state-of-the-art question generators expect the answer to the question they should generate as part of the input.
For example:
In paragraph: "... The difference is fluid friction, both within the fluid itself and between the fluid and its surroundings, which we call viscosity. ..."
In answer: "viscosity"
Out question: "How do we call the difference in fluid friction?"
The main goal of the thesis is therefore to extract such answer candidates (e.g. "viscosity“) from a given paragraph of text. This task has already been addressed with supervised and unsupervised models on a variety of datasets (e.g. SQuAD) which can be seen as a starting point and as a strong baseline. Finally, the thesis may also investigate how transferable the developed approaches are to other educational datasets.
Task
- In-depth review of the related work of answer candidate selection
- Iteratively developing a model that extracts answer candidates on SQuAD
- Automatically evaluating the model in comparison to state-of-the-art baselines
- Automatic or empirical evaluation of your implemented approach on an educational dataset to investigate its generalizability.
Requirements
- Interest in Educational Technologies
- Experience in NLP / Statistical Learning (e.g. Contextualized Word Embeddings, Classifier Evaluation)
Initial Literature
download corresponding tendering
Keywords: NLU, information extraction
Research Area(s):
Tutor: Steuer,
