  Pooling-based recurrent neural architectures consistently outperform their
counterparts without pooling. However, the reasons for their enhanced
performance are largely unexamined. In this work, we examine three commonly
used pooling techniques (mean-pooling, max-pooling, and attention), and propose
max-attention, a novel variant that effectively captures interactions among
predictive tokens in a sentence. We find that pooling-based architectures
substantially differ from their non-pooling equivalents in their learning
ability and positional biases--which elucidate their performance benefits. By
analyzing the gradient propagation, we discover that pooling facilitates better
gradient flow compared to BiLSTMs. Further, we expose how BiLSTMs are
positionally biased towards tokens in the beginning and the end of a sequence.
Pooling alleviates such biases. Consequently, we identify settings where
pooling offers large benefits: (i) in low resource scenarios, and (ii) when
important words lie towards the middle of the sentence. Among the pooling
techniques studied, max-attention is the most effective, resulting in
significant performance gains on several text classification tasks.
