FLIN: A Flexible Natural Language Interface for Web Navigation
This addresses the challenge of flexible web navigation for users, though it is incremental as it builds on existing semantic parsing methods.
The paper tackles the problem of AI assistants adapting to diverse websites without constant retraining by proposing FLIN, a natural language interface that maps user commands to concept-level actions, and results show it successfully adapts to new websites within domains.
AI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained. We propose FLIN, a natural language interface for web navigation that maps user commands to concept-level actions (rather than low-level UI actions), thus being able to flexibly adapt to different websites and handle their transient nature. We frame this as a ranking problem: given a user command and a webpage, FLIN learns to score the most relevant navigation instruction (involving action and parameter values). To train and evaluate FLIN, we collect a dataset using nine popular websites from three domains. Our results show that FLIN was able to adapt to new websites in a given domain.