IntelLanG - Intelligent Information Processing and Natural Language Generation

Co-located with ECAI 2020

August 29, 2020, Santiago de Compostela, Spain

Ensorsed by SIGGEN: The ACL Special Interest Group in Natural Language Generation

News! Submission deadline extended to May 15, 2020, in view of the postponement of the ECAI conference due to ongoing efforts to contain the spread of COVID-19.

The main objective of the field of Natural Language Generation (NLG) is to help in the production of systems that allow the automatic creation of high-quality text from non-linguistic information. NLG is currently one of the most relevant research areas for contemporary Artificial Intelligence.

The general problem of NLG is very challenging because the transformation of input data into the final output text involves multiple sub-tasks, such as determining the content and structure of the message or choosing the right words to express the final text. And this is done in diverse application areas where the goal of the system varies: generation of informative texts, summaries, simplified texts, persuasive texts, recommendations, narratives or conversational turns in dialogue systems…

The use of intelligent data and information processing techniques can help in many relevant aspects of the NLG problem, for example in the contribution of formalisms for knowledge modeling and management, KDD, Data Mining and Machine Learning techniques and tools for the analysis of data, or in the development of models for the evaluation of the quality of the proposals, among many others. Artificial Intelligence information processing techniques can also gain a lot from their interaction with the particular area of ​​NLG, as is the case with the burgeoning field of explainable artificial intelligence, where rendering explanations of complex AI models in natural language is a relatively under-explored area.

The objective of this workshop is to identify challenges and to evaluate current results that arise from the interaction of intelligent information processing techniques with techniques and knowledge within the field of Natural Language Generation, both at the level of models and applications.