%0 Conference Proceedings %T Grounding Open-Domain Instructions to Automate Web Support Tasks %A Xu, Nancy %A Masling, Sam %A Du, Michael %A Campagna, Giovanni %A Heck, Larry %A Landay, James %A Lam, Monica %Y Toutanova, Kristina %Y Rumshisky, Anna %Y Zettlemoyer, Luke %Y Hakkani-Tur, Dilek %Y Beltagy, Iz %Y Bethard, Steven %Y Cotterell, Ryan %Y Chakraborty, Tanmoy %Y Zhou, Yichao %S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %D 2021 %8 June %I Association for Computational Linguistics %C Online %F xu-etal-2021-grounding %X Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people. We build RUSS (Rapid Universal Support Service) to tackle this problem. RUSS consists of two models: First, a BERT-LSTM with pointers parses instructions to WebLang, a domain-specific language we design for grounding natural language on the web. Then, a grounding model retrieves the unique IDs of any webpage elements requested in the WebLang. RUSS may interact with the user through a dialogue (e.g. ask for an address) or execute a web operation (e.g. click a button) inside the web runtime. To augment training, we synthesize natural language instructions mapped to WebLang. Our dataset consists of 80 different customer service problems from help websites, with a total of 741 step-by-step instructions and their corresponding actions. RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single instructions. It outperforms state-of-the-art models that directly map instructions to actions without WebLang. Our user study shows that RUSS is preferred by actual users over web navigation. %R 10.18653/v1/2021.naacl-main.80 %U https://aclanthology.org/2021.naacl-main.80 %U https://doi.org/10.18653/v1/2021.naacl-main.80 %P 1022-1032