  End-to-end generation-based approaches have been investigated and applied in
task-oriented dialogue systems. However, in industrial scenarios, existing
methods face the bottlenecks of controllability (e.g., domain-inconsistent
responses, repetition problem, etc) and efficiency (e.g., long computation
time, etc). In this paper, we propose a task-oriented dialogue system via
action-level generation. Specifically, we first construct dialogue actions from
large-scale dialogues and represent each natural language (NL) response as a
sequence of dialogue actions. Further, we train a Sequence-to-Sequence model
which takes the dialogue history as input and outputs sequence of dialogue
actions. The generated dialogue actions are transformed into verbal responses.
Experimental results show that our light-weighted method achieves competitive
performance, and has the advantage of controllability and efficiency.
