Dynamic Feature Selection with Attention in Incremental Parsing

COLING 2018 · Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto ·

One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context. In this work, we present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature selection. Our model learns, for example, which tokens should a parser focus on, to decide the next action. Our multilingual experiment shows its effectiveness across many languages. We also present an experiment with augmented test dataset and demon- strate it helps to understand the model{'}s behavior on locally ambiguous points.