Cerence Complex Question Answering system
Projekt: Cerence Complex Question Answering system
“Question Answering & Chatbots”; Winter Semester 2019/2020; supervised by Prof. Dr. Andreas Both
- Modul / Lehrveranstaltung: Question Answering & Chatbots
- Semester: 2. Semester
- Jahr: 2020
- URL: http://webengineering.ins.hs-anhalt.de:41210/chat
The Cerence Complex Question Answering System is intended to work in the automotive industry. Since there are many systems that are capable of answering “simple questions” - the main goal of this system is to answer non-trivial questions using such features as coreference resolution and geospatial questions. Imagine the driver asks the Question Answering system: “What I see around me” and the system gives an answer based on the driver’s geolocation.
An example of non-trivial dialog:
- Q1: Who was the director of Django Unchained?
- A1: The director is Quentin Tarantino.
- Q2: Tell me more about it.
- A2: Django Unchained is a 2012 American western film …
In this example the goal is to link "Django Unchained" from Q1 and it from Q2 to give more information about the coreference entity.The Cerence Complex Question Answering system is designed in accordance with microservice architecture and based on DBpedia data. Technologies that were used: Python, Tensorflow, HuggingFace, Docker, Gunicorn, Stardog and the Qanary framework.
Project partner: Cerence Inc., Kuldeep Singh
- Aleksandr Perevalov
- Kirill Davydov
- Thomas Fritz
- Artur Mikhailov
- Aleksandr Vysokov
Link to implementation: http://webengineering.ins.hs-anhalt.de:41210/chat