Pre-trained language models (PLMs) have achieved great success in NLP and have
recently been used for tasks in computational semantics. However, these tasks do not fully …

We present an end-to-end neural approach to generate English sentences from formal
meaning representations, Discourse Representation Structures (DRSs). We use a rather …

The neural turn in computational linguistics has made it relatively easy to build systems for
natural language generation, as long as suitable annotated corpora are available. But can …

A bare meaning representation can be expressed in various ways using natural language,
depending on how the information is structured on the surface level. We are interested in …

Some applications of artificial intelligence make it desirable that logical formulae be
converted computationally to comprehensible natural language sentences. As there are …

Neural networks are notoriously data-hungry. This represents an issue in cases where data
are scarce such as in low-resource languages. Data augmentation is a technique commonly …

Human groups of all sizes and kinds engage in deliberation, problem solving, strategizing,
decision making, and more generally, cognition. Some groups are large, and that setting …

Logic-to-text generation is an important yet underrepresented area of natural language
generation (NLG). In particular, most previous works on this topic lack sound evaluation. We …

Chunking means splitting the sentences into tokens and then grouping them in a meaningful
way. When it comes to high-performance chunking systems, transformer models have …

Neural networks have yielded great breakthroughs in NLP in recent years, but the vast
majority of research has focused on literal language, while modelling text attributes, or style …