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.

This paper describes a new evaluation metric, TER-Plus (TERp) for automatic evaluation of machine translation (MT). TERp is an extension of Translation Edit Rate (TER). It builds on the success of TER as an evaluation metric and alignment tool and addresses several of its weaknesses through the use of paraphrases, stemming, synonyms, as well as edit costs that can be automatically optimized to correlate better with various types of human judgments. We present a correlation study comparing TERp to BLEU, METEOR and TER, and illustrate that TERp can better evaluate translation adequacy.

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Automated methods for identifying whether sentences are grammatical have various potential applications (e.g., machine translation, automated essay scoring, computer-assisted language learning). In this work, we construct a statistical model of grammaticality using various linguistic features (e.g., misspelling counts, parser outputs, n-gram language model scores). We also present a new publicly available dataset of learner sentences judged for grammaticality on an ordinal scale. In evaluations, we compare our system to the one from Post (2011) and find that our approach yields state-of-the-art performance.