This paper presents a systematic review of benchmarks and approaches for explainability in
Machine Reading Comprehension (MRC). We present how the representation and …

The technology development cycle continues to accelerate, and novelty analysis is
becoming increasingly important for R&D planning as well as in the patent application …

Training a model to provide natural language explanations (NLEs) for its predictions usually
requires the acquisition of task-specific NLEs, which is time-and resource-consuming. A …

We propose an explainable inference approach for science questions by reasoning on
grounding and abstract inference chains. This paper frames question answering as a natural …

Mathematical statements written in natural language are usually composed of two different
modalities: mathematical elements and natural language. These two modalities have …

An emerging line of research in Explainable NLP is the creation of datasets enriched with
human-annotated explanations and rationales, used to build and evaluate models with step …

We propose a novel approach for answering and explaining multiple-choice science
questions by reasoning on grounding and abstract inference chains. This paper frames …

When performing complex multi-step reasoning tasks, the ability of Large Language Models
(LLMs) to derive structured intermediate proof steps is important for ensuring that the models …

When performing complex multi-step reasoning tasks, the ability of Large Language Models
(LLMs) to derive structured intermediate proof steps is important for ensuring that the models …

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face
recognition and natural language processing, the emergence of general machine …