The web provides a rich, open-domain environment with textual, structural, and spatial properties. We propose a new task for grounding language in this environment: given a natural language command (e.g., "click on the second article"), choose the correct element on the web page (e.g., a hyperlink or text box). We collected a dataset of over 50,000 commands that capture various phenomena such as functional references (e.g. "find who made this site"), relational reasoning (e.g. "article by john"), and visual reasoning (e.g. "top-most article"). We also implemented and analyzed three baseline models that capture different phenomena present in the dataset.
@article{arxiv.1808.09132,
title = {Mapping Natural Language Commands to Web Elements},
author = {Panupong Pasupat and Tian-Shun Jiang and Evan Zheran Liu and Kelvin Guu and Percy Liang},
journal= {arXiv preprint arXiv:1808.09132},
year = {2018}
}