分解を通じて適応的な言語インターフェースを学習する
Learning Adaptive Language Interfaces through Decomposition
ユーザーが高次の指示を低次のステップに分解して教えることで、ロボットが新しい抽象概念を対話的に学習する意味解析システムを提案した論文。
著者: Siddharth Karamcheti, Dorsa Sadigh, Percy Liang
分類: cs.CL, cs.AI, cs.LG, cs.RO
原文アブストラクト
Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand. Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples. Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods. Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught. At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge. We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.
