A Continuously Growing Dataset of Sentential Paraphrases

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ~70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.Comment: 11 pages, accepted to EMNLP 201

Theoretical and Empirical Studies of Productivity Growth in the Agricultural Economics –– Cases of China and the United States

AbstractThis article investigates agricultural productivity growth over several decades, emphasizing to a great extent the agricultural economic development condition for the nine agricultural divisions of the United States, and China's 27 provinces in terms of Malmquist productivity growth index. The paper sets up a technique to make use of two-stage linear programming method, based on sequential production technology, to estimate the most fitted and reliable distance functions in relevant agricultural sectors, and thus to compute the Malmquist productivity indexes. Especially, it proposes to decompose the productivity growth index into two major components, technical progress and efficiency improvement, and their sub-components, to study the sources of growth in productivity