2,875 research outputs found
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
Academic Integrity Resources - links and guides
an online tutorial, a pdf version, a powerpoint presentation, links to regulations
Plagiarism Detection: Keeping Check on Misuse of Intellectual Property
Today, Plagiarism has become a menace. Every journal editor or conference organizers has to deal with this problem. Simply Copying or rephrasing of text without giving due credit to the original author has become more common. This is considered to be an Intellectual Property Theft. We are developing a Plagiarism Detection Tool which would deal with this problem. In this paper we discuss the common tools available to detect plagiarism and their short comings and the advantages of our tool over these tools
Paraphrase Generation with Deep Reinforcement Learning
Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.Comment: EMNLP 201
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