EAGER: Combining natural language inference and data-driven paraphrasing

自然语言推理(NLI)和数据驱动的释义具有共同的相关目标，即能够检测两个自然语言表达之间的语义关系，并能够对输入文本进行重写，以使结果文本具有相同的意义但表达方式不同。一方面，在自然语言输入中识别语篇蕴涵(RTE)的工作试图将确定自然语言假设是否由自然语言前提(有时称为自然逻辑)蕴涵的过程形式化。另一方面，数据驱动的转述研究试图在各种粒度级别提取转述，包括词汇转述(简单的同义词)、短语转述、短语模板(或“推理规则”)和句子转述，用于各种下游应用，如问题回答、信息提取、文本生成和摘要。这个渴望的奖项探索了通过同步上下文无关文法(SCFGs)对句子转述的分析，以及它们如何与形式约束相结合，类似于最近基于短语的RTE自然逻辑公式中的工作。数据驱动的释义在很大程度上忽略了语义形式化，而NLI严重依赖于手工制作的资源，如WordNet。如果这个项目成功，它可能会导致NLI系统更健壮，以及释义系统更好地形式化。总而言之，这些改进将使更好的实时教育系统得以开发。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界面，并进一步促进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.