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Neuronal paraphras generation attracts attention lately in various application scenarios. The task is often formalized as a sequence-to-sequence learning problem. Prakash et al. (2016) In the SEQ2SEQ model, engage in a stacked residual LSTM network in the SEQ2SEQ model. Enlarge the model capacity. Cao et al. (2017) Use an additional vocabulary to restrict word candidates during generation. Gupta et al. (2018) Use a variable auto encoder framework to create more different paraphrases. Ma et al. (2018) Use an attention layer instead of a linear mapping in the decoder to accommodate word candidates. Iyyer et al. (2018) Syntactic information on controllable paraphras generation. Zhang and Lapata (2017) A similar task of punishment simplification with SEQ2SEQ model coupled with deep reinforcing learning, in which the reward function is manually defined for the task. Similar to these works, we also introduce the Paraphrave generator with SEQ2SEQ framework. The Gaugebart It is on questions to ask for the exercise of the generator through reinforcing learning.
There are also on paraphrazer generation in different settings, for example, Mallinson et al (2017) using bilingual data to produce paraphrases by using a shared translation in another language Wieting et al (2017) ; .... Wieting and Gimpel (2018) Use the neuronal machine translation into paraphrases over the back-translation of bilingual sentence pairs. Buck et al. (2018) and Dong et al. (2017) The problem of QA-specific paraphrasing with the instructions from an external QA system and an associated evatar.
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