Automated Answer Validation using Text Similarity
This work addresses the problem of improving learning outcomes and accessibility in question answering systems for learners, but it appears incremental as it builds on existing text similarity approaches.
The paper tackled automated answer validation by implementing Siamese neural network models to provide a generalized solution, comparing them with other text similarity methods.
Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available. There have been some works in science question answering which show that information retrieval methods outperform neural methods, especially in the multiple choice version of this problem. We implement Siamese neural network models and produce a generalised solution to this problem. We compare our supervised model with other text similarity based solutions.