Explaining Interactions Between Text Spans
Sagnik Ray Choudhury Pepa Atanasova Isabelle Augenstein
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.