  Dialogue policy transfer enables us to build dialogue policies in a target
domain with little data by leveraging knowledge from a source domain with
plenty of data. Dialogue sentences are usually represented by speech-acts and
domain slots, and the dialogue policy transfer is usually achieved by assigning
a slot mapping matrix based on human heuristics. However, existing dialogue
policy transfer methods cannot transfer across dialogue domains with different
speech-acts, for example, between systems built by different companies. Also,
they depend on either common slots or slot entropy, which are not available
when the source and target slots are totally disjoint and no database is
available to calculate the slot entropy. To solve this problem, we propose a
Policy tRansfer across dOMaIns and SpEech-acts (PROMISE) model, which is able
to transfer dialogue policies across domains with different speech-acts and
disjoint slots. The PROMISE model can learn to align different speech-acts and
slots simultaneously, and it does not require common slots or the calculation
of the slot entropy. Experiments on both real-world dialogue data and
simulations demonstrate that PROMISE model can effectively transfer dialogue
policies across domains with different speech-acts and disjoint slots.
