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

摘要

Many deep learning architectures have been proposed to model the composition-ality in text sequences, requiring a 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.