摘要
随着人工智能应用的普及,与生活相关的各种服务都在向着智能化发展,铁路客服作为旅客业务咨询的重要窗口,高效的服务能够带给旅客更优的体验。为了能够方便旅客咨询出行相关的问题,铁路12306开通了在线咨询服务。12306在线咨询服务的应答能力主要依赖于问题的检索能力,能够高效、准确地检索旅客咨询的问题是提升在线客服服务的关键。因此该文利用倒排索引技术和LSTM孪生网络提出了一种新的检索方法,首先利用倒排索引技术进行文本的预处理,可以极大地提高检索的效率;然后基于LSTM孪生网络和注意力机制构建了AT_LSTM模型用于计算问题的相似度,并且与基于HowNet和基于词向量余弦距离的相似度算法进行了对比。实验结果表明,该检索优化方法相比HowNet和Consine+word2vec准确率都有较大的提升。智能应答检索能力的优化,提升了客运的在线咨询服务能力,铁路客运可以更好地服务旅客。
With the popularization of artificial intelligence application,various life-related services are developing towards intelligence.As an important window for passenger business consultation,railway customer service can bring passengers a better experience.In order to make it easier for passengers to consult on travel-related issues,railway 12306 has launched an online consultation service.The response ability of 12306 online consultation service mainly depends on the retrieval ability of questions.The key to improve online customer service is to be able to retrieve the questions inquired by passengers efficiently and accurately.Therefore,we propose a new retrieval method based on inverted index technology and LSTM Siamase network.Firstly,the inverted index technology is used to preprocess text,which can greatly improve the retrieval efficiency.Then the AT_LSTM model is constructed based on the LSTM Siamase network and attention mechanism to calculate the similarity of the problem,and is compared with the similarity algorithm based on HowNet and word vector cosine distance.Experimental results show that the proposed method has a great improvement in accuracy compared with HowNet and Consine+Word2Vec.The optimization of intelligent response retrieval ability enhances the online consultation service ability of passenger transport,and the railway passenger transport can better serve passengers.
作者
杨立鹏
廉文彬
季续国
李雯
陈华龙
YANG Li-peng;LIAN Wen-bin;JI Xu-guo;LI Wen;CHEN Hua-long(China Academy of Railway Sciences Co.,Ltd.,Beijing 100081,China;China State Railway Group Co.,Ltd.,Beijing 100844,China;Visionary Intelligence Technology Co.,Ltd.,Beijing 100044,China)
出处
《计算机技术与发展》
2022年第7期149-154,共6页
Computer Technology and Development
基金
国家重点研发计划(2020YFF0304101)。
关键词
深度学习
孪生网络
智能客服
倒排索引
自然语言处理
deep learning
Siamase LSTM
intelligent customer service
inverted index
natural language processing