Deterministic Attention for Sequence-to-Sequence Constituent Parsing

Abstract

The sequence-to-sequence model is proven to be extremely successful in constituent parsing. It relies on one key technique, the probabilistic attention mechanism, to automatically select the context for prediction. Despite its successes, the probabilistic attention model does not always select the most important context. For example, the headword and boundary words of a subtree have been shown to be critical when predicting the constituent label of the subtree, but this contextual information becomes increasingly difficult to learn as the length of the sequence increases. In this study, we proposed a deterministic attention mechanism that deterministically selects the important context and is not affected by the sequence length. We implemented two different instances of this framework. When combined with a novel bottom-up linearization method, our parser demonstrated better performance than that achieved by the sequence-to-sequence parser with probabilistic attention mechanism.