# Automated Answer Validation using Text Similarity

arXiv: https://arxiv.org/abs/2401.08688 · HTML: https://mlsanity.com/paper/2401.08688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [Question Answering via Integer Programming over Semi-Structured Knowledge](https://mlsanity.com/paper/1604.06076/) (2016)
- [Interpreting BERT-based Text Similarity via Activation and Saliency Maps](https://mlsanity.com/paper/2208.06612/) (2022)
- [Science Question Answering using Instructional Materials](https://mlsanity.com/paper/1602.04375/) (2016)

## Abstract (arXiv)

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.