Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
Vered Shwartz, Ido Dagan · 2018
Revealing the implicit semantic relation between the constituents of a nouncompound is important for many NLP applications.It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations.Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds.We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases.Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.