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Automated Answer Validation using Text Similarity (2401.08688v1)
Published 13 Jan 2024 in cs.CL and cs.IR
Abstract: 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.
- Gradio. https://www.gradio.app/. Accessed: Dec 07, 2023.
- Signature verification using a" siamese" time delay neural network. Advances in neural information processing systems.
- Combining retrieval, statistics, and inference to answer elementary science questions. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 30.
- A study of the knowledge base requirements for passing an elementary science test. In Proceedings of the 2013 workshop on Automated knowledge base construction, pages 37–42.
- Sujatha Das Gollapalli and See Kiong Ng. 2022. Qsts: A question-sensitive text similarity measure for question generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3835–3846.
- Sanket Gupta. 2018. Overview of text similarity metrics in python.
- Question answering via integer programming over semi-structured knowledge. arXiv preprint arXiv:1604.06076.
- Exploring markov logic networks for question answering. In Proceedings of the 2015 conference on empirical methods in natural language processing, pages 685–694.
- Using semantic text similarity calculation for question matching in a rheumatoid arthritis question-answering system. Quantitative Imaging in Medicine and Surgery, 13(4):2183.
- Yang Li and Peter Clark. 2015. Answering elementary science questions by constructing coherent scenes using background knowledge. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2007–2012.
- Edward Loper and Steven Bird. 2002. Nltk: The natural language toolkit. arXiv preprint cs/0205028.
- Interpreting bert-based text similarity via activation and saliency maps. In Proceedings of the ACM Web Conference 2022, pages 3259–3268.
- Wes McKinney et al. 2011. pandas: a foundational python library for data analysis and statistics. Python for high performance and scientific computing, 14(9):1–9.
- Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
- Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.
- Science question answering using instructional materials. arXiv preprint arXiv:1602.04375.
- Prabhnoor Singh. 2019. Siamese network keras for image and text similarity.
- Streamlit Team. 2023. Streamlit. https://www.streamlit.io/. Accessed: Dec 07, 2023.
- Evaluating the examiner: The perils of pearson correlation for validating text similarity metrics. In Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association, pages 130–138.
- D Viji and S Revathy. 2022. A hybrid approach of weighted fine-tuned bert extraction with deep siamese bi–lstm model for semantic text similarity identification. Multimedia Tools and Applications, 81(5):6131–6157.
- Crowdsourcing multiple choice science questions. ArXiv, abs/1707.06209.
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