EAGER: Combining natural language inference and data-driven paraphrasing

EAGER：结合自然语言推理和数据驱动的释义

• 批准号: 1249516
• 负责人: Benjamin Van Durme
• 金额: $ 9.95万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1249516&HistoricalAwards=false
• 关键词: EAGER; Combining; natural; language; inference

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.

自然语言推理（NLI）和数据驱动的释义共享相关目标，即能够检测两个自然语言表达式之间的语义关系，并能够重新编写输入文本，以便生成的文本意义相同但措辞不同。 一方面，在NLI中识别文本蕴涵（RTE）的工作试图将确定自然语言假设是否由自然语言前提所蕴涵的过程形式化，有时称为“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（简单同义词），短语释义，短语模板（或“推理规则”），以及解释性释义，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奖探讨了弥合差距，通过分析通过同步上下文无关文法（SCFGs），以及它们如何可能耦合到正式的限制，类似于最近的工作，在基于短语的公式的自然逻辑的RTE。 数据驱动的释义在很大程度上忽略了语义形式主义，NLI严重依赖手工制作的资源，如WordNet。 如果这个项目是成功的，它将有可能导致NLI系统，更强大的，和释义系统，更好地形式化。 综合考虑，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，如网络搜索和自然语言接口到移动的设备，并进一步计算语义学和形式语言学之间的联系。