Abstract
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.- Anthology ID:
- 2021.naacl-main.222
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2777–2788
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.222
- DOI:
- 10.18653/v1/2021.naacl-main.222
- Cite (ACL):
- Sahisnu Mazumder and Oriana Riva. 2021. FLIN: A Flexible Natural Language Interface for Web Navigation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2777–2788, Online. Association for Computational Linguistics.
- Cite (Informal):
- FLIN: A Flexible Natural Language Interface for Web Navigation (Mazumder & Riva, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.naacl-main.222.pdf