AI for Text Understanding

Dear Colleagues, 

This Special Issue on “Artificial Intelligence for Text Understanding” aims to focus on the different aspects of AI involved when free unstructured text is automatically converted into structured data including, but not limited to, commonsense reasoning, automated reasoning and inference, ethical AI, heuristic search, knowledge representation, machine learning, and natural language processing.

In the field of natural language processing, the recent introduction of the Transformer model has truly changed the way we model textual data and has advanced the state-of-the-art to include a wide range of NLP tasks. These transformer models, as well as other deep learning approaches, however, also raise a number of issues and concerns within the NLP community. Although these machine learning models often lead to very exciting results, they function as black boxes, i.e., the resulting models and output are hard to interpret. In addition, NLP has evolved from an academic research field to a widely adopted technology in industry, marketing, recruiting, media, and politics in the last decade. As a result, researchers have started asking questions about possible harmful applications and bias in both the training data and in the application of machine learning models in and outside of academia. The most recent generation of machine learning approaches are also extremely data-greedy, making it  often hard for low(er)-resourced languages to keep pace with NLP advancements for English and other well-resourced languages. Finally, we observe the limitations of purely lexical language modeling for more complex linguistic tasks such as irony detection, event detection, or coreference  resolution, which clearly require more advanced commonsense or deep linguistic knowledge.

For this Special Issue, we welcome papers describing state-of-the-art approaches for all of the different aspects of AI for text understanding, with a special focus on approaches addressing the aforementioned topics:

• Overview papers of the current state-of-the-art of NLP tasks;
• Explainable machine learning approaches;
• Approaches integrating linguistic information or commonsense reasoning into deep learning networks;
• Cross-lingual approaches for transfer of knowledge from well-resourced to low(er)-resourced languages, including code-mixing;
• Transfer approaches for domain-specific data and applications;
• Handling bias in AI methodologies.

Dr. Els Lefever
Prof. Dr. Veronique Hoste
Guest Editors

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• cross-lingual NLP
• transfer learning in NLP
• explainable machine learning for NLP
• ethics in NLP
• commonsense reasoning
• integration of linguistic and commonsense information in deep learning for NLP