We present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature selection.
Aug 20, 2018 · In this work, we present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- ...
This work presents a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature ...
Table of Contents · Abstract · 1 Introduction · 2 Model. 2.1 Base model; 2.2 Attention on local features · 3 Experiments. 3.1 Parser; 3.2 Multilingual evaluation ...
One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context.
Dynamic feature selection with attention in incremental parsing. Ryosuke Kohita; Hiroshi Noji; et al. 2018; COLING 2018. Focus areas. Focus areas.
One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context.
GDFS involves learning two separate networks: one responsible for making predictions (the predictor) and one responsible for making selections (the policy).
Missing: Attention Incremental Parsing.
We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging "equivalent" stacks based on feature values.
Oct 6, 2021 · The attention mechanism is a part of a neural architecture that enables to dynamically highlight relevant features of the input data, which, in ...