When Are Tree Structures Necessary for Deep Learning of Representations?

Understand

Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture.

• But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate.
• In this paper we benchmark {\bf recursive} neural models against sequential {\bf recurrent} neural models (simple recurrent and LSTM models), enforcing apples-to-apples comparison as much as possible.
• We investigate 4 tasks: (1) sentiment classification at the sentence level and phrase level; (2) matching questions to answer-phrases; (3) discourse parsing; (4) semantic relation extraction (e.g., {\em component-whole} between nouns).