Comparison of Turkish Paraphrase Generation Models

An intuitive way for a human to write paraphrase sentences is to replace words or phrases in the original sentence with their corresponding synonyms and make necessary changes to ensure the new sentences are fluent and grammatically correct. We propose a novel approach to modeling the process with dictionary-guided editing networks which effectively conduct rewriting on the source sentence to generate paraphrase sentences. It jointly learns the selection of the appropriate word level and phrase level paraphrase pairs in the context of the original sentence from an off-the-shelf dictionary as well as the generation of fluent natural language sentences. Specifically, the system retrieves a set of word level and phrase level paraphrase pairs derived from the Paraphrase Database (PPDB) for the original sentence, which is used to guide the decision of which the words might be deleted or inserted with the soft attention mechanism under the sequence-to-sequence framework. We conduct experiments on two benchmark datasets for paraphrase generation, namely the MSCOCO and Quora dataset. The automatic evaluation results demonstrate that our dictionary-guided editing networks outperforms the baseline methods. On human evaluation, results indicate that the generated paraphrases are grammatically correct and relevant to the input sentence.

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Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods

Computational Linguistics ◽

10.1162/coli_a_00002 ◽

2010 ◽

Vol 36 (3) ◽

pp. 341-387 ◽

Cited By ~ 65

Author(s):

Nitin Madnani ◽

Bonnie J. Dorr

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Recent Work ◽

Language Processing ◽

Data Driven ◽

Future Trends ◽

Automatic Construction ◽

Work Done ◽

Paraphrase Generation ◽

Potential Use

The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language—words, phrases, and sentences—is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications. In this article, we attempt to conduct a comprehensive and application-independent survey of data-driven phrasal and sentential paraphrase generation methods, while also conveying an appreciation for the importance and potential use of paraphrases in the field of NLP research. Recent work done in manual and automatic construction of paraphrase corpora is also examined. We also discuss the strategies used for evaluating paraphrase generation techniques and briefly explore some future trends in paraphrase generation.

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