论文标题
预测的句子嵌入和可读性得分的预测预测
Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores
论文作者
论文摘要
在许多应用中预测书籍的潜在成功至关重要。这可以帮助出版商和读者在决策过程中是否值得出版和阅读。在本文中，我们提出了一个模型，该模型利用预验证的句子嵌入以及各种可读性得分来进行书籍成功预测。与以前的方法不同，所提出的方法不需要基于计数的，词汇或句法特征。取而代之的是，我们在验证的句子嵌入方式上使用卷积神经网络，并通过简单的串联操作利用不同的可读性得分。我们提出的模型在此任务上的表现高达6.4 \％f1得分点。此外，我们的实验表明，根据我们的模型，只有前1K句子足以预测书籍的潜在成功。
Predicting the potential success of a book in advance is vital in many applications. This could help both publishers and readers in their decision-making process whether or not a book is worth publishing and reading, respectively. In this paper, we propose a model that leverages pretrained sentence embeddings along with various readability scores for book success prediction. Unlike previous methods, the proposed method requires no count-based, lexical, or syntactic features. Instead, we use a convolutional neural network over pretrained sentence embeddings and leverage different readability scores through a simple concatenation operation. Our proposed model outperforms strong baselines for this task by as large as 6.4\% F1-score points. Moreover, our experiments show that according to our model, only the first 1K sentences are good enough to predict the potential success of books.
