NAtural LOgic Meets MAchine Learning (NALOMA) is the first workshop of its kind, aiming to bridge the gap between Machine Learning and Natural Logic. It will take place from July 12-July 17, 2020, during the 9th North American Summer School for Logic, Language, and Information (NASSLLI) at Brandeis University in Waltham, Massachusetts.
Recent models of Natural Language Inference (NLI) have made considerable progress in the last couple of years and have achieved performance comparable to human-level. Even though this last statement might still be an exaggeration, it is indeed true that NLI models are capable of doing more things than we thought they would some years ago. On the other hand, research on symbolic methods for NLI has not been fully abandoned. One such area that is still flourishing is research on Natural Logic. There is actually renewed interest in monotonicity inference, and connections with theorem provers and tableau systems from standard areas of logic.
Within this context, the aim of this workshop is to bring together researchers working in both Natural Logic and Machine Learning approaches to NLI, initiating a discussion with the two sets of researchers that have been largely unconnected up to now.