For the stable optimization of deep neural networks, regularization methods such as dropout and batch normalization have been used in various tasks. Nevertheless, the correct position to apply dropout has rarely been discussed, and different positions have been employed depending on the practitioners. In this study, we investigate the correct position to apply dropout. We demonstrate that for a residual network with batch normalization, applying dropout at certain positions increases the performance, whereas applying dropout at other positions decreases the performance. Based on theoretical analysis, we provide the following guideline for the correct position to apply dropout: apply one dropout after the last batch normalization but before the last weight layer in the residual branch. We provide detailed theoretical explanations to support this claim and demonstrate them through module tests. In addition, we investigate the correct position of dropout in the head that produces the final prediction. Although the current consensus is to apply dropout after global average pooling, we prove that applying dropout before global average pooling leads to a more stable output. The proposed guidelines are validated through experiments using different datasets and models.

Math word problem solving is an emerging research topic in Natural Language Processing.Recently, to address the math word problem solving task, researchers have applied the encoderdecoder architecture, which is mainly used in machine translation tasks.The state-of-the-art neural models use hand-crafted features and are based on generation methods.In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use handcrafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge.To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction.To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer.Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1% in MAWPS, and 62.5% in DRAW-1K, and reached comparable performance of 82.1% in ALG514 dataset.

Solving algebraic word problems has recently emerged as an important natural language processing task.To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using 'Op (operator/operand)' tokens as a unit of input/output.However, such a neural model suffered two issues: expression fragmentation and operand-context separation.To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) 'Expression' token and (2) operand-context pointers when generating solution equations.The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS.Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3% on ALG514, 59.5% on DRAW-1K, and 84.5% on MAWPS.The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operandcontext separation.(2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40%.