  Answer selection (AS) is a critical subtask of the open-domain question
answering (QA) problem. The present paper proposes a method called RLAS-BIABC
for AS, which is established on attention mechanism-based long short-term
memory (LSTM) and the bidirectional encoder representations from transformers
(BERT) word embedding, enriched by an improved artificial bee colony (ABC)
algorithm for pretraining and a reinforcement learning-based algorithm for
training backpropagation (BP) algorithm. BERT can be comprised in downstream
work and fine-tuned as a united task-specific architecture, and the pretrained
BERT model can grab different linguistic effects. Existing algorithms typically
train the AS model with positive-negative pairs for a two-class classifier. A
positive pair contains a question and a genuine answer, while a negative one
includes a question and a fake answer. The output should be one for positive
and zero for negative pairs. Typically, negative pairs are more than positive,
leading to an imbalanced classification that drastically reduces system
performance. To deal with it, we define classification as a sequential
decision-making process in which the agent takes a sample at each step and
classifies it. For each classification operation, the agent receives a reward,
in which the prize of the majority class is less than the reward of the
minority class. Ultimately, the agent finds the optimal value for the policy
weights. We initialize the policy weights with the improved ABC algorithm. The
initial value technique can prevent problems such as getting stuck in the local
optimum. Although ABC serves well in most tasks, there is still a weakness in
the ABC algorithm that disregards the fitness of related pairs of individuals
in discovering a neighboring food source position.
