Pooling-based recurrent neural architectures, like max-attention, are better at understanding sentences than those without pooling. They help the model learn better and reduce biases towards the beginning and end of a sentence. This is especially useful when there is not much data available or when important words are in the middle of a sentence. Max-attention pooling is the most effective technique, improving performance on text classification tasks.