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The evaluation of paraphrase generation has similar difficulties as the evaluation of machine translation. The quality of a paraphrase depends on its context, whether it is being used as a summary, and how it is generated, among other factors. Additionally, a good paraphrase usually is lexically dissimilar from its source phrase.
A paraphrase can be introduced with verbum dicendi—a declaratory expression to signal the transition to the paraphrase. For example, in "The author states 'The signal was red,' that is, the train was not allowed to proceed," the that is signals the paraphrase that follows. A paraphrase does not need to accompany a direct quotation. [20]
Download as PDF; Printable version; In other projects ... QuillBot is a software developed in 2017 that uses artificial intelligence to rewrite and paraphrase text ...
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.Their creation was inspired by biological neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]
For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular. The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
GPT-1 improved on previous best-performing models by 4.2% on semantic similarity (or paraphrase detection), evaluating the ability to predict whether two sentences are paraphrases of one another, using the Quora Question Pairs (QQP) dataset.
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.