  In everyday communication, where-questions are answered by place
descriptions. To answer where-questions automatically, computers should be able
to generate relevant place descriptions that satisfy inquirers' information
needs. Human-generated answers to where-questions constructed based on a few
anchor places that characterize the location of inquired places. The challenge
for automatically generating such relevant responses stems from selecting
relevant anchor places. In this paper, we present templates that allow to
characterize the human-generated answers and to imitate their structure. These
templates are patterns of generic geographic information derived and encoded
from the largest available machine comprehension dataset, MS MARCO v2.1. In our
approach, the toponyms in the questions and answers of the dataset are encoded
into sequences of generic information. Next, sequence prediction methods are
used to model the relation between the generic information in the questions and
their answers. Finally, we evaluate the performance of predicting templates for
answers to where-questions.

Information Retrieval