tailieunhanh - Báo cáo khoa học: "An Empirical Evaluation of Data-Driven Paraphrase Generation Techniques"

Paraphrase generation is an important task that has received a great deal of interest recently. Proposed data-driven solutions to the problem have ranged from simple approaches that make minimal use of NLP tools to more complex approaches that rely on numerous language-dependent resources. Despite all of the attention, there have been very few direct empirical evaluations comparing the merits of the different approaches. | An Empirical Evaluation of Data-Driven Paraphrase Generation Techniques Donald Metzler Information Sciences Institute Univ. of Southern California Marina del Rey CA USA metzler@ Eduard Hovy Information Sciences Institute Univ. of Southern California Marina del Rey CA USA hovy@ Chunliang Zhang Information Sciences Institute Univ. of Southern California Marina del Rey CA USA czheng@ Abstract Paraphrase generation is an important task that has received a great deal of interest recently. Proposed data-driven solutions to the problem have ranged from simple approaches that make minimal use of NLP tools to more complex approaches that rely on numerous language-dependent resources. Despite all of the attention there have been very few direct empirical evaluations comparing the merits of the different approaches. This paper empirically examines the tradeoffs between simple and sophisticated paraphrase harvesting approaches to help shed light on their strengths and weaknesses. Our evaluation reveals that very simple approaches fare surprisingly well and have a number of distinct advantages including strong precision good coverage and low redundancy. 1 Introduction A popular idiom states that variety is the spice of life . As with life variety also adds spice and appeal to language. Paraphrases make it possible to express the same meaning in an almost unbounded number of ways. While variety prevents language from being overly rigid and boring it also makes it difficult to algorithmically determine if two phrases or sentences express the same meaning. In an attempt to address this problem a great deal of recent research has focused on identifying generating and harvesting phrase- and sentence-level paraphrases Barzi-lay and McKeown 2001 Bhagat and Ravichan-dran 2008 Barzilay and Lee 2003 Bannard and Callison-Burch 2005 Callison-Burch 2008 Lin 546 and Pantel 2001 Pang et al. 2003 Pasca and Dienes 2005 Many data-driven approaches to the paraphrase problem .

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