XTE：可解释的文本蕴涵
XTE: Explainable Text Entailment
摘要：
文本蕴涵（Text entailment）是确定一段文本是否在逻辑上遵循另一段文本的任务，是NLP中的关键组件，为许多语义应用提供输入，例如问答，文本摘要，信息抽取和机器翻译等。蕴涵场景的范围可以从简单的语法变化到文本片段之间更复杂的语义关系，但大多数方法都尝试一种通用的解决方案，通常有利于某些场景而不利于另一个场景。此外，对于需要世界知识的蕴涵，大多数系统仍然作为“黑盒子”工作，提供一个是/否的答案，但不能解释潜在的推理过程。在这项工作中，我们介绍了XTE -可解释的文本蕴涵-一种新的复合方法识别文本蕴涵分析的蕴涵对，以决定它是否必须解决语法或语义。此外，如果涉及语义匹配，我们使答案可解释，使用外部知识库组成的结构化词汇定义，以产生自然语言的理由，解释的语义关系之间的文本片段。除了优于成熟的蕴涵算法外，我们的复合方法还向可解释的人工智能迈出了重要的一步，允许推理模型解释，使语义推理过程明确和可理解。
英文摘要：
Text entailment, the task of determining whether a piece of text logically follows from another piece of text, is a key component in NLP, providing input for many semantic applications such as question answering, text summarization, information extraction, and machine translation, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. Furthermore, for entailments requiring world knowledge, most systems still work as a "black box", providing a yes/no answer that does not explain the underlying reasoning process. In this work, we introduce XTE - Explainable Text Entailment - a novel composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. Also, if a semantic matching is involved, we make the answer interpretable, using external knowledge bases composed of structured lexical definitions to generate natural language justifications that explain the semantic relationship holding between the pieces of text. Besides outperforming well-established entailment algorithms, our composite approach gives an important step towards Explainable AI, allowing the inference model interpretation, making the semantic reasoning process explicit and understandable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
