Despite significant progress in text generation models, a serious limitation
is their tendency to produce text that is factually inconsistent with
information in the input. Recent work has studied whether textual entailment
systems can be used to identify factual errors; however, these sentence-level
entailment models are trained to solve a different problem than generation
filtering and they do not localize which part of a generation is non-factual.
In this paper, we propose a new formulation of entailment that decomposes it at
the level of dependency arcs. Rather than focusing on aggregate decisions, we
instead ask whether the semantic relationship manifested by individual
dependency arcs in the generated output is supported by the input. Human
judgments on this task are difficult to obtain; we therefore propose a method
to automatically create data based on existing entailment or paraphrase
corpora. Experiments show that our dependency arc entailment model trained on
this data can identify factual inconsistencies in paraphrasing and
summarization better than sentence-level methods or those based on question
generation, while additionally localizing the erroneous parts of the
generation.

本文提出了一种新的蕴含形式，即在依赖弧的级别上对其进行分解，以便更好地检测文本生成模型中的事实不一致性。实验证明，基于依赖弧的蕴含模型可以更好地识别重新表述和摘要中的事实不一致性，并在定位错误部分方面具有优势。