Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
This addresses a key challenge in NLP for interpreting infrequent or new noun-compounds, though it appears incremental as it builds on existing paraphrasing methods.
The paper tackled the problem of revealing implicit semantic relations in noun-compounds for NLP applications by proposing a neural model that represents paraphrases in a continuous space, improving performance on paraphrasing and classification tasks.
Revealing the implicit semantic relation between the constituents of a noun-compound 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.