Pooling is a mechanism used in deep learning models, especially in natural language processing (NLP) tasks, to reduce the dimensionality of representations while retaining essential information. Pooling is often applied to handle variable-length input sequences, such as text sequences. Pooling plays a crucial role in handling variable-length text sequences, capturing essential features, and improving the efficiency of the model by reducing the number of parameters and computations.

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Baseline Needs More Love: On SimpleWord-Embedding-Based Models and Associated Pooling Mechanisms

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Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Based upon this understanding, we propose two additional pooling strategies over learned word embeddings: (i) a max-pooling operation for improved interpretability; and (ii) a hierarchical pooling operation, which preserves spatial (n-gram) information within text sequences. We present experiments on 17 datasets encompassing three tasks: (i) (long) document classification; (ii) text sequence matching; and (iii) short text tasks, including classification and tagging.

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