This survey paper proposes a clearer view of natural language reasoning in the field of
Natural Language Processing (NLP), both conceptually and practically. Conceptually, we …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Fact verification systems typically rely on neural network classifiers for veracity prediction,
which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to …

In settings from fact-checking to question answering, we frequently want to know whether a
collection of evidence (premises) entails a hypothesis. Existing methods primarily focus on …

Transformers have been shown to be able to perform deductive reasoning on a logical
rulebase containing rules and statements written in natural language. Recent works show …

Current abstractive summarization models either suffer from a lack of clear interpretability or
provide incomplete rationales by only highlighting parts of the source document. To this end …

Knowing the reasoning chains from knowledge to the predicted answers can help construct
an explainable question answering (QA) system. Advances on QA explanation propose to …

Recent studies on transformer-based language models show that they can answer
questions by reasoning over knowledge provided as part of the context (ie, in-context …

Transformers have recently been shown to be capable of reliably performing logical
reasoning over facts and rules expressed in natural language, but abductive reasoning …

Logical reasoning is central to human cognition and intelligence. It includes deductive,
inductive, and abductive reasoning. Past research of logical reasoning within AI uses formal …