Abstract
Extracting action sequences from texts is
challenging, as it requires commonsense inferences based on world
knowledge. Although there has been work on extracting action scripts,
instructions, navigation actions, etc., they require either the
set of candidate actions be provided in advance, or action
descriptions are restricted to a specific form, e.g., description
templates. In this paper we aim to extract action sequences from
texts in \emph{free} natural language, i.e., without any restricted
templates, provided the set of actions is unknown. We
propose to extract action sequences from texts based on the deep
reinforcement learning framework. Specifically, we view ``selecting''
or ``eliminating'' words from texts as ``actions'', and texts associated with actions as ``states''. We build Q-networks to learn policies of extracting actions and extract plans from the labeled texts. We demonstrate the effectiveness of our approach on several datasets with comparison to state-of-the-art approaches.
Keywords
- Natural Language Processing: Natural Language Processing
- Planning and Scheduling: Activity and Plan Recognition
- Planning and Scheduling: Planning with Incomplete information
- Machine Learning: Deep Learning
