Reinforcement Learning-based Answer Selection with Class Imbalance Handling and Efficient Differential Evolution Initialization

Answer selection (AS) involves the task of selecting the best answer from a given list of potential options. Current methods commonly approach the AS problem as a binary classification task, using pairs of positive and negative samples. However, the number of negative samples is usually much larger than the positive ones, resulting in a class imbalance. Training on imbalanced data can negatively impact classifier performance. To address this issue, a novel reinforcement learning-based technique is proposed in this study. In this approach, the AS problem is formulated as a sequence of sequential decisions, where an agent classifies each received instance and receives a reward at each step. To handle the class imbalance, the reward assigned to the majority class is lower than that for the minority class. The parameters of the policy are initialized using an improved Differential Evolution (DE) technique. To enhance the efficiency of the DE algorithm, a novel cluster-based mutation operator is introduced. This operator utilizes the K-means clustering approach to identify the winning cluster and employs an upgrade strategy to incorporate potentially viable solutions into the existing population. For word embedding, the DistilBERT model is utilized, which reduces the size of the BERT (Bidirectional encoder representations from transformers) model by 40% and improves computational efficiency by running 60% faster. Despite the decrease, the DistilBERT model maintains 97% of its language comprehension abilities by utilizing knowledge distillation in the pretraining phase. Extensive experiments are carried out on LegalQA, TrecQA, and WikiQA datasets to assess the suggested model. The outcomes showcase the superiority of the proposed model over existing techniques in the domain of AS.

@article{Wei2023,
  title     = {Reinforcement Learning-based Answer Selection with Class Imbalance Handling and Efficient Differential Evolution Initialization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jia Wei},
  doi       = {10.14569/IJACSA.2023.0141014},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141014}
}