  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 ThingTalk, 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 ThingTalk. 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 ThingTalk. 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 ThingTalk. Our user study shows that RUSS is
preferred by actual users over web navigation.
