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Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition

机译:复述识别为概率准同步识别

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摘要

We present a novel approach to deciding whether two sentences hold a paraphrase relationship. We employ a generative model that generates a paraphrase of a given sentence, and we use probabilistic inference to reason about whether two sentences share the paraphrase relationship. The model cleanly incorporates both syntax and lexical semantics using quasi-synchronous dependency grammars (Smith and Eisner, 2006). Furthermore, using a product of experts (Hinton, 2002), we combine the model with a complementary logistic regression model based on state-of-the-art lexical overlap features. We evaluate our models on the task of distinguishing true paraphrase pairs from false ones on a standard corpus, giving competitive state-of-the-art performance.
机译:我们提出一种新颖的方法来确定两个句子是否具有释义关系。我们使用生成模型来生成给定句子的释义,并且使用概率推断来推断两个句子是否共享释义关系。该模型使用准同步依赖语法将语法和词汇语义完美地结合在一起(Smith和Eisner,2006)。此外,使用专家产品(Hinton,2002年),我们将模型与基于最新词汇重叠特征的互补逻辑回归模型结合在一起。我们评估模型的目的是区分标准语料库上的真释义对与假释义对,从而提供具有竞争力的最新性能。

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