A General Framework for Learning to Paraphrase in Learner Workbooks


A General Framework for Learning to Paraphrase in Learner Workbooks – We present a new approach for learning to paraphrasing, which aims to learn a system that combines natural language processing, reinforcement learning and automatic reasoning with a multi-agent system to effectively mimic the language of human beings. Our approach utilizes a deep learning technique applied at the core of a machine learning framework, which consists of multiple agents. When applied to a natural language processing module, the model learns to paraphrase its natural language and, as a consequence, improve its paraphrasing performance. We also present a novel learning strategy for a multi-agent system, that uses a reinforcement learning strategy to learn to paraphrase its input phrases. Experiments on a large-scale synthetic language translation task show that our approach can translate natural language sentences successfully to human speech recognition tasks, and outperform the standard English Paraphrase and UnParaphrase systems, both of which have been widely used.

What is the essence of a word? This question was posed in a previous paper, and it has received much attention in the context of machine translation. In this paper, we propose a novel method for translating and analyzing such words in order to extract linguistic information from text. While a few techniques have been proposed in previous studies to extract more information from text, they either ignore certain semantic properties of texts or lack such information in other data. In this paper, we are interested in how to learn useful knowledge for extracting information from texts and combining it to perform a translation. We propose to use a new approach based on a deep neural network (DNN) for this purpose. Our proposed method achieves state-of-the-art results in all the experiments.

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A General Framework for Learning to Paraphrase in Learner Workbooks

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  • Multi-view Graph Convolutional Neural Network

    You want to see the rain forest, rain forestWhat is the essence of a word? This question was posed in a previous paper, and it has received much attention in the context of machine translation. In this paper, we propose a novel method for translating and analyzing such words in order to extract linguistic information from text. While a few techniques have been proposed in previous studies to extract more information from text, they either ignore certain semantic properties of texts or lack such information in other data. In this paper, we are interested in how to learn useful knowledge for extracting information from texts and combining it to perform a translation. We propose to use a new approach based on a deep neural network (DNN) for this purpose. Our proposed method achieves state-of-the-art results in all the experiments.


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