In this paper, we represent a new recursive neural network architecture that fits squarely in this tradition, but aims to solve a number of difficulties that have arisen in existing work.
the model we propose addresses three issues: 1. how to make the composition functions adaptive, in the sense that they operate adequately for the many different types of combinations (e.g., adjective-noun combinations are of a very different type than VP-PP combinations); 2. how to deal with different branching factors of nodes in the relevant syntactic trees (i.e., we want to avoid having to binarize syntactic trees, 1 but also do not want ternary productions to be completely independent from binary productions); 3. how to deal with uncertainty about the correct parse inside the neural architecture (i.e., we don't want to work with just the best or k-best parses for a sentence according to an external model, but receive an entire distribution over possible parsers).