Lead
Intelligent software agents will soon be engaging in question-and-answer dialogues with us, as well as providing us with information and entertainment. To enable such interactive applications, this project is investigating fundamental methods of machine speech comprehension and learning.

Lay summary

Conversational agents require a series of reading capabilities. They need to be able to identify references to persons, organisations, events and concepts – comparable to automatically creating Wikipedia links. Despite the fundamental ambiguity and context sensitivity of our language, they must be able to understand who or what is being referred to. The facility to recognise and extract the content of statements is also essential. Recent advances in deep learning provide ways of understanding the meaning of sentences independently of the choice of words and sentence structure. The ultimate aim is to develop suitable discourse models which, in combination with situational understanding, permit effective interaction with humans.

Numerous different algorithms help us to procure and evaluate information, including in the context of search engines or social media. The same applies in the areas of entertainment or e-commerce. Intelligent, conversational systems to which we can direct spoken queries in natural language are being used to an increasing extent. The economic and social consequences of these new technologies are enormous.

Conversational software must be able to understand text and language automatically. This means more than just handling spoken queries. A deeper understanding of the documents and texts containing the world’s knowledge is also called for. The aim of this project is to develop new methods of text comprehension: What is the subject matter and what is being said? What could be relevant or interesting to a user? These are questions that demand an algorithmic solution.