Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated textual
explanations. These explanations are used downstream in three ways: as data …

Verifying complex political claims is a challenging task, especially when politicians use
various tactics to subtly misrepresent the facts. Automatic fact-checking systems fall short …

The ability to convey relevant and faithful information is critical for many tasks in conditional
generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal …

Abstract Conversational Information Seeking (CIS) is a relatively new research area within
conversational AI that attempts to seek information from end-users in order to understand …

The integration of multi-document pre-training objectives into language models has resulted
in remarkable improvements in multi-document downstream tasks. In this work, we propose …

In this paper we introduce a new class of software tools engaged in delivering successful
explanations of complex processes on top of basic Explainable AI (XAI) software systems …

This paper presents a new method for objectively measuring the explainability of textual
information, such as the outputs of Explainable AI (XAI). We introduce a metric called …

Many real-world applications (eg, note taking, search) require extracting a sentence or
paragraph from a document and showing that snippet to a human outside of the source …

Disagreement in natural language annotation has mostly been studied from a perspective of
biases introduced by the annotators and the annotation frameworks. Here, we propose to …

Asking questions about a situation is an inherent step towards understanding it. To this end,
we introduce the task of role question generation, which, given a predicate mention and a …