Applications of Sequence to Sequence Models for Technical Support Automation

Juniper Networks, Inc. offers hardware products and software services to its enterprise customers. Due to the nature of it's business, Juniper Networks, Inc. is deeply invested in providing the best customer support and as part of the support automation team, our goal is to optimize the company...

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Bibliographic Details
Published in2018 IEEE International Conference on Big Data (Big Data) pp. 4861 - 4869
Main Authors Aalipour, Ghodrat, Kumar, Pranav, Aditham, Santosh, Nguyen, Trung, Sood, Aditya
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Attention Mechanism
Automation
Bi-LSTM
Business
ChatBot
Computer architecture
Context modeling
Data models
Deep learning
Seq2seq
Text Summarization
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Summary:Juniper Networks, Inc. offers hardware products and software services to its enterprise customers. Due to the nature of it's business, Juniper Networks, Inc. is deeply invested in providing the best customer support and as part of the support automation team, our goal is to optimize the company's efforts towards it. For this purpose, alongside other initiatives, we leverage deep learning based sequence to sequence models wherever we see fit. In this paper, we discuss two such models: a conversational chatbot to help answer some technical questions for our customers, and a text summarizer to condense the large text in our support tickets and other articles. These two models are designed using bi-directional recurrent neural network (Bi-RNN) architectures for content understanding and were customized to fit the domain-specific nature of our data. First, we discuss our efforts towards data preparation. Then, we explain our model design, customization and evaluation mechanisms. Finally, we provide the preliminary results and share the potential impact our models will have on our business. Our initial results have BLEU score of 0.21 for text summarizer which is 16% better than our baseline model. Our chatbot passed the eye-tests of our subject matter experts.
DOI:10.1109/BigData.2018.8622395