Abstract
Interactive structured prediction (ISP) is an emerging framework for structured prediction (SP) where the
user and the system collaborate to produce a high quality output. Typically, search algorithms applied
to ISP problems have been based on the algorithms for fully-automatic SP systems. However, the decision
rule applied should not be considered as optimal since the goal in ISP is to reduce human effort instead of
output errors. In this work, we present some insight into the theory of the sequential ISP search problem.
First, it is formulated as a decision theory problem from which a general analytical formulation of the opti-
mal decision rule is derived. Then, it is compared with the standard formulation to establish under what
conditions the standard algorithm should perform similarly to the optimal decision rule. Finally, a general
and practical implementation is given and evaluated against three classical ISP problems: interactive
machine translation, interactive handwritten text recognition, and interactive speech recognition.