Abstract
Scientific claim verification can help the researchers easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Because there are a huge amount of papers in the corpus, most of the existing scientific claim verification solutions are always in a two-stage manner that first roughly detects a set of candidate related papers by some naïve but fast methods such as some similarity measures, and then utilizes the large but relatively slow deep neural models for accurate classification. To improve the recall of the overall system by improving the recall of the rough abstract retrieval stage, we propose an approach that also utilizes the neural classification model for the rough retrieval stage. To improve the scalability of the proposal, we propose a distillation-based method to obtain a lightweight model for the rough retrieval stage. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.
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This works was partially supported by 23H03402.
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Zhang, Z., Li, J., Fukumoto, F. (2023). An Efficient Approach for Improving the Recall of Rough Abstract Retrieval in Scientific Claim Verification. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_6
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