CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant
arXiv:1905.01978
Abstract
We propose a large scale semantic parsing dataset focused on instruction-driven communication with an agent in Minecraft. We describe the data collection process which yields additional 35K human generated instructions with their semantic annotations. We report the performance of three baseline models and find that while a dataset of this size helps us train a usable instruction parser, it still poses interesting generalization challenges which we hope will help develop better and more robust models.
References in corpus (9)
- Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
- Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
- Control of Memory, Active Perception, and Action in Minecraft
- A Deep Hierarchical Approach to Lifelong Learning in Minecraft
- Language to Logical Form with Neural Attention
- Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
- Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
- Data Recombination for Neural Semantic Parsing
- Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft