Designing a Recurrent Neural Network to extract sentiment from tweets is a very hard task. When using memory cells in their design, the task becomes even harder due to the large number of design alternatives and the costly process of finding a performant design. In this paper we propose an original evolutionary algorithm to address the hard challenge of discovering novel Recurrent Neural Network memory cell designs for sentiment analysis on tweets. We used three different tasks to discover and evaluate the designs. We conducted experiments and the results show that the best obtained designs surpass the baselines—which are the most popular cells, LSTM and GRU. During the discovery process we evaluated roughly 17,000 cell designs. The selected winning candidate outperformed the others for the overall sentiment analysis problem, hence showing generality. We made the winner selection by using the cumulated accuracies on all three considered tasks.

中文翻译：

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发现用于推文情感分析的新型记忆单元设计

设计一个循环神经网络来从推文中提取情感是一项非常艰巨的任务。在其设计中使用存储单元时，由于存在大量设计备选方案以及寻找高性能设计的成本高昂的过程，因此任务变得更加困难。在本文中，我们提出了一种原始进化算法，以解决发现用于推文情感分析的新型循环神经网络内存单元设计的艰巨挑战。我们使用了三个不同的任务来发现和评估设计。我们进行了实验，结果表明获得的最佳设计超过了基线——这是最受欢迎的单元、LSTM 和 GRU。在发现过程中，我们评估了大约 17,000 个电池设计。选定的获胜候选人在整体情感分析问题上的表现优于其他候选人，因此表现出一般性。我们通过使用所有三个考虑任务的累积准确度来选择获胜者。