3 research outputs found
MOSS: End-to-End Dialog System Framework with Modular Supervision
A major bottleneck in training end-to-end task-oriented dialog system is the
lack of data. To utilize limited training data more efficiently, we propose
Modular Supervision Network (MOSS), an encoder-decoder training framework that
could incorporate supervision from various intermediate dialog system modules
including natural language understanding, dialog state tracking, dialog policy
learning, and natural language generation. With only 60% of the training data,
MOSS-all (i.e., MOSS with supervision from all four dialog modules) outperforms
state-of-the-art models on CamRest676. Moreover, introducing modular
supervision has even bigger benefits when the dialog task has a more complex
dialog state and action space. With only 40% of the training data, MOSS-all
outperforms the state-of-the-art model on a complex laptop network
troubleshooting dataset, LaptopNetwork, that we introduced. LaptopNetwork
consists of conversations between real customers and customer service agents in
Chinese. Moreover, MOSS framework can accommodate dialogs that have supervision
from different dialog modules at both the framework level and model level.
Therefore, MOSS is extremely flexible to update in a real-world deployment
Improving Support Ticket Systems Using Machine Learning: A Literature Review
Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field