Reasoning over spans of tokens from different parts of the input is essential
for natural language understanding (NLU) tasks such as fact-checking (FC),
machine reading comprehension (MRC) or natural language inference (NLI).
However, existing highlight-based explanations primarily focus on identifying
individual important tokens or interactions only between adjacent tokens or
tuples of tokens. Most notably, there is a lack of annotations capturing the
human decision-making process w.r.t. the necessary interactions for informed
decision-making in such tasks. To bridge this gap, we introduce SpanEx, a
multi-annotator dataset of human span interaction explanations for two NLU
tasks: NLI and FC. We then investigate the decision-making processes of
multiple fine-tuned large language models in terms of the employed connections
between spans in separate parts of the input and compare them to the human
reasoning processes. Finally, we present a novel community detection based
unsupervised method to extract such interaction explanations from a model's
inner workings.

通过 SpanEx 数据集和社区检测方法，研究了自然语言理解中的跨度推理，包括事实核查和机器阅读理解，并与人类推理过程进行了比较。