E-mail is considered the commonly used and efficient way of communication over the globe. In the corporate sectors, the number of E-mails received every day is considerably high and the timely response to every E-mail is essential. Several researchers believe that natural language processing (NLP) techniques by the use of deep learning (DL) architectures have played a considerable part to reduce manual work for repeated E-mail responses and intended to develop E-mail systems with intelligent response function. In this view, this paper designs an intelligent DL enabled optimal bidirectional long short term memory (Bi-LSTM) technique for an automated E-mail reply (OBiLSTM-AER) of E-mail Client Prototype. The goal of the proposed model is to provide an automated E-mail reply solution for persons as well as corporates which receive massive identical E-mails daily. The presented model employs Glove and OBiLSTM model for feature extraction of receiving and response E-mails respectively. Finally, softmax classifier is applied to allocate the class labels. For improving the performance of the BiLSTM model, the hyperparameter tuning process takes place using an oppositional glowworm swarm optimization (OGSO) algorithm. An extensive set of simulations were performed to highlight the betterment of the proposed method and the results are examined interms of distinct measures.

中文翻译：

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基于智能深度学习的双向长短期记忆模型，用于电子邮件客户端原型自动回复


电子邮件被认为是全球常用且有效的通信方式。在企业部门，每天收到的电子邮件数量相当多，及时回复每封电子邮件至关重要。一些研究人员认为，利用深度学习（DL）架构的自然语言处理（NLP）技术在减少重复电子邮件回复的手工工作方面发挥了相当大的作用，并旨在开发具有智能回复功能的电子邮件系统。鉴于此，本文设计了一种智能深度学习支持的最佳双向长期短期记忆 (Bi-LSTM) 技术，用于电子邮件客户端原型的自动电子邮件回复 (OBiLSTM-AER)。所提出模型的目标是为每天收到大量相同电子邮件的个人和企业提供自动电子邮件回复解决方案。所提出的模型采用 Glove 和 OBiLSTM 模型分别对接收和回复电子邮件进行特征提取。最后，应用softmax分类器来分配类标签。为了提高 BiLSTM 模型的性能，超参数调整过程使用对立萤火虫群优化 (OGSO) 算法进行。进行了一系列广泛的模拟，以突出所提出方法的改进，并根据不同的措施对结果进行检查。