  Factual consistency is one of the most important requirements when editing
high quality documents. It is extremely important for automatic text generation
systems like summarization, question answering, dialog modeling, and language
modeling. Still, automated factual inconsistency detection is rather
under-studied. Existing work has focused on (a) finding fake news keeping a
knowledge base in context, or (b) detecting broad contradiction (as part of
natural language inference literature). However, there has been no work on
detecting and explaining types of factual inconsistencies in text, without any
knowledge base in context. In this paper, we leverage existing work in
linguistics to formally define five types of factual inconsistencies. Based on
this categorization, we contribute a novel dataset, FICLE (Factual
Inconsistency CLassification with Explanation), with ~8K samples where each
sample consists of two sentences (claim and context) annotated with type and
span of inconsistency. When the inconsistency relates to an entity type, it is
labeled as well at two levels (coarse and fine-grained). Further, we leverage
this dataset to train a pipeline of four neural models to predict inconsistency
type with explanations, given a (claim, context) sentence pair. Explanations
include inconsistent claim fact triple, inconsistent context span, inconsistent
claim component, coarse and fine-grained inconsistent entity types. The
proposed system first predicts inconsistent spans from claim and context; and
then uses them to predict inconsistency types and inconsistent entity types
(when inconsistency is due to entities). We experiment with multiple
Transformer-based natural language classification as well as generative models,
and find that DeBERTa performs the best. Our proposed methods provide a
weighted F1 of ~87% for inconsistency type classification across the five
classes.
