Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Spatial relations are a basic part of human cognition. However, they are expressed in
natural language in a variety of ways, and previous work has suggested that current vision …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

This paper reviews the state-of-the-art of language models architectures and strategies for"
complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large …

Large language models (LLMs) and vision-language models (VLMs) have demonstrated
remarkable performance across a wide range of tasks and domains. Despite this promise …

Spatial commonsense, the knowledge about spatial position and relationship between
objects (like the relative size of a lion and a girl, and the position of a boy relative to a bicycle …

Inferring spatial relations in natural language is a crucial ability an intelligent system should
possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19) …

Recent research shows synthetic data as a source of supervision helps pretrained language
models (PLM) transfer learning to new target tasks/domains. However, this idea is less …

Qualitative spatial reasoning has been a core research topic in GIScience and AI for
decades. It has been adopted in a wide range of applications such as wayfinding, question …

Large language models (LLMs) have exhibited impressive performance in language
comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a …