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

Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment