EAGER: Combining natural language inference and data-driven paraphrasing

Johns Hopkins University, Baltimore MD

Investigators

Abstract

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.