Learning to Compose Words into Sentences with Reinforcement LearningLearning to Compose Words into Sentences with Reinforcement LearningYogatama, Dani and Blunsom, Phil and Dyer, Chris and Grefenstette, Edward and Ling, Wang2016
Paper summarymarekThe aim is to have the system discover a method for parsing that would benefit a downstream task.
They construct a neural shift-reduce parser – as it’s moving through the sentence, it can either shift the word to the stack or reduce two words on top of the stack by combining them. A Tree-LSTM is used for composing the nodes recursively. The whole system is trained using reinforcement learning, based on an objective function of the downstream task. The model learns parse rules that are beneficial for that specific task, either without any prior knowledge of parsing or by initially training it to act as a regular parser.
Learning to Compose Words into Sentences with Reinforcement Learning
arXiv e-Print archive - 2016 via Local Bibsonomy