EAGER: Combining natural language inference and data-driven paraphrasing

自然语言推理（NLI）和数据驱动的释义都有一个共同的目标，即能够检测两个自然语言表达式之间的语义关系，能够改写输入文本，使结果文本意义相等，但措词不同。一方面，在NLI中识别文本蕴涵（RTE）的工作试图形式化确定自然语言假设是否被自然语言前提所蕴涵的过程，有时被称为“自然逻辑”。另一方面，数据驱动的释义研究试图在各种粒度级别上提取释义，包括词汇释义（简单的同义词）、短语释义、短语模板（或“推理规则”）和句子释义，用于各种下游应用，如问答、信息提取、文本生成和摘要。这个EAGER奖项通过分析同步上下文无关语法（scfg）的句子释义，以及它们如何与形式约束相结合，类似于最近在RTE中基于短语的自然逻辑公式的工作，探索弥合差距。数据驱动的释义在很大程度上忽略了语义形式，NLI严重依赖于手工制作的资源，比如WordNet。如果这个项目是成功的，它将有可能导致更健壮的NLI系统，以及更好形式化的释义系统。综上所述，这些改进将有助于开发更好的RTE系统。此外，该项目有可能影响广泛使用的人类语言技术，如网络搜索和移动设备的自然语言接口，并进一步推动计算语义学和形式语言学之间的联系。

Natural language inference (NLI) and data-driven paraphrasing share the related goals of being able to detect the semantic relationship between two natural language expressions, and being able to re-word an input text so that the resulting text is meaning-equivalent but worded differently. On the one hand, work in recognizing textual entailment (RTE) within NLI has attempted to formalize the process of determining whether a natural language hypothesis is entailed by a natural language premise, sometimes called "natural logic". Research in data-driven paraphrasing, on the other hand, attempts to extract paraphrases at a variety of levels of granularity including lexical paraphrases (simple synonyms), phrasal paraphrases, phrasal templates (or "inference rules"), and sentential paraphrases, for various downstream applications such as question answering, information extraction, text generation, and summarization.This EAGER award explores bridging the gap, through analysis of sentential paraphrasing via synchronous context free grammars (SCFGs), and how they may be coupled to formal constraints akin to recent work in phrase-based formulations of natural logic for RTE. Data-driven paraphrasing has largely neglected semantic formalisms, and NLI has relied heavily on hand-crafted resources like WordNet. If this project is successful it will potentially lead towards NLI systems that are more robust, and paraphrasing systems that are better formalized. Taken together, these improvements will allow better RTE systems to be developed. Moreover, this project has the potential to impact widely used human language technologies such as web search and natural language interfaces to mobile devices, and to further the connection between computational semantics and formal linguistics.