There has been an ever-growing interest in tasks targeting Natural Language Understanding and Reasoning. Although deep learning models have achieved human-like performance in many such tasks, it has also been repeatedly shown that they lack the precision, generalization power, reasoning capabilities, and explainability found in more traditional, symbolic approaches. Thus, current research has started employing hybrid methods, combining the strengths of each tradition and mitigating its weaknesses. This workshop would like to promote this research direction and foster fruitful dialog between the two disciplines by bringing together researchers working on hybrid methods in any subfield of Natural Language Understanding and Reasoning.

NALOMA began by focusing on bridging the gap between machine learning and natural logic but has since broadened to include works integrating symbolic methods and machine learning. Even so, combining deep learning with natural logic remains a highly promising direction. The remarkable capabilities of LLMs, which directly operate on natural language expressions, offer a distinct advantage to natural logic—a family of logics with formulas resembling natural language expressions.

The NALOMA workshop is endorsed by SIGSEM.