  Combining the representations of the words that make up a sentence into a
cohesive whole is difficult, since it needs to account for the order of words,
and to establish how the words present relate to each other. The solution we
propose consists in iteratively adjusting the context. Our algorithm starts
with a presumably erroneous value of the context, and adjusts this value with
respect to the tokens at hand. In order to achieve this, representations of
words are built combining their symbolic embedding with a positional encoding
into single vectors. The algorithm then iteratively weighs and aggregates these
vectors using our novel second-order attention mechanism. Our models report
strong results in several well-known text classification tasks.
