© unsplash/@katiemoum

© unsplash/@katiemoum
Government & Public Sector

Automated public services

Context

Public administration in Germany suffers from a shortage of young talent. According to a McKinsey study, this shortage will amount to 731.000 employees by 2030. At the same time, public authorities are often recognized as old fashioned regarding their work processes. Digitalizing public services could save costs, reduce waiting times and maintain a satisfactory service level even with a reduced number of employees. One burden to overcome is the administrative jargon which is often not intuitive to citizens.

Challenges

The automation of public and governmental services requires an user interface, where users can query their needs and the service provides all related relevant information. This can be quite challenging, since the user queries contain informal formulations of the customer needs, which may differ strongly from the administrative jargon. Thus, the automated service has to unterstand the meaning of the queries and translate them to administrative jargon.

For example, the registration of a business requires an industry code, for which the automated service should understand the relation between the business purpose entered by the client and the descriptions of different industry codes. For example, if a user wants to register a beauty salon, he/she could enter "I lacquer nails" and should find the right business code for beauty salons.

This is especially challenging if a business code is including a number of very diverse services. For instance, the business code "Miscellaneous other services" includes piercing studios as well as facility management. Traditional keyword based approaches are therefore not promising to deliver good results.

Potential solution approaches

The intention of the user queries can be resolved by implementing a model based on the pre-trained BERT model (bidirectional encoder representations from transformers), developed by Google. The key advantage of the BERT model is, that it is able to recognize semantically similar words and expressions by considering their context. The base model is then further specialized for the specific context by training on problem- and domain-specific labeled data, such as user queries and their respective correct business code.

Related webinars

Text recognition (OCR) - The first step on the way to a successful implementation of an NLP project

In this talk we will deal with the topic of text recognition.

Ewelina Fiebig

Machine Learning Scientist

Fabian Gringel

Machine Learning Scientist

Labeling Tools - The second step on the way to the successful implementation of an NLP project

The success of an NLP project consists of a series of steps from data preparation to modeling and deployment. Since the input data are often scanned documents, the data preparation step initially involves the use of text recognition tools (OCR for short) and later on also the use of so-called labeling tools. In this webinar we will deal with the topic of selecting a suitable labeling tool.

Ewelina Fiebig

Machine Learning Scientist

Fabian Gringel

Machine Learning Scientist

Semantic search and understanding of natural text with neural networks: BERT

In this webinar you will get an introduction to the application of BERT for Semantic Search using a real case study: Every year millions of citizens interact with public authorities and are regularly overwhelmed by the technical language used there. We have successfully used BERT to deliver the right answer from government documents with the help of colloquial queries - without having to use technical terms in the queries.

Konrad Schultka

Machine Learning Scientist

Jona Welsch

Machine Learning Scientist

Automated answering of questions with neural networks: BERT

In this webinar we will present a method based on the BERT model for automated answering of questions.

Mattes Mollenhauer

Machine Learning Scientist

Recurrent neural networks: How computers learn to read

The webinar will give an introduction to the functioning of RNNs and illustrate their use in an example project from the field of legal tech

Fabian Gringel

Machine Learning Scientist