论文标题
现代法国诗歌一代与罗伯塔和GPT-2
Modern French Poetry Generation with RoBERTa and GPT-2
论文作者
论文摘要
我们为法语中的现代诗歌产生了一种新颖的神经模型。该模型由两个预审预定的神经模型组成，这些模型是针对诗歌生成任务进行微调的。该模型的编码器是基于罗伯塔的编码器，而解码器基于GPT-2。这样，模型就可以受益于罗伯塔（Roberta）的优质自然语言表现以及GPT-2的良好自然语言生成表现。我们的评估表明，该模型可以成功创造法国诗歌。以5分的比例，人类法官对输出诗的典型性和情感性给出了最低的3.57分数，而最佳分数为3.79。
We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are fine-tuned for the poem generation task. The encoder of the model is a RoBERTa based one while the decoder is based on GPT-2. This way the model can benefit from the superior natural language understanding performance of RoBERTa and the good natural language generation performance of GPT-2. Our evaluation shows that the model can create French poetry successfully. On a 5 point scale, the lowest score of 3.57 was given by human judges to typicality and emotionality of the output poetry while the best score of 3.79 was given to understandability.
