  Response generation for task-oriented dialogues involves two basic
components: dialogue planning and surface realization. These two components,
however, have a discrepancy in their objectives, i.e., task completion and
language quality. To deal with such discrepancy, conditioned response
generation has been introduced where the generation process is factorized into
action decision and language generation via explicit action representations. To
obtain action representations, recent studies learn latent actions in an
unsupervised manner based on the utterance lexical similarity. Such an action
learning approach is prone to diversities of language surfaces, which may
impinge task completion and language quality. To address this issue, we propose
multi-stage adaptive latent action learning (MALA) that learns semantic latent
actions by distinguishing the effects of utterances on dialogue progress. We
model the utterance effect using the transition of dialogue states caused by
the utterance and develop a semantic similarity measurement that estimates
whether utterances have similar effects. For learning semantic actions on
domains without dialogue states, MsALA extends the semantic similarity
measurement across domains progressively, i.e., from aligning shared actions to
learning domain-specific actions. Experiments using multi-domain datasets, SMD
and MultiWOZ, show that our proposed model achieves consistent improvements
over the baselines models in terms of both task completion and language
quality.
