Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven Dialogue
Table 2: Results of task with 2 slots and 3 slots
['[EMPTY]', '2 slots Avg. reward', '2 slots Avg. length', '3 slots Avg. reward', '3 slots Avg. length']
reward” is the average of total reward and “Avg. length” is the average number of turns for each dialogue. “our model*” is the result by human evaluation. It can be seen that our model achieved consistent higher average total rewards and lower average lengths than those in LSTM-DQN agent and MDP method in all tasks. In 2 slot task, LSTM-DQN and our model have similar performance. In 3 slots task, our model outperforms LSTM-DQN significantly. Average total reward of LSTM-DQN falls by 40.3% from 2 slots to 3 slots, while our model falls by 7.9%, this shows the robustness of our model. In human evaluation, our model also achieves good performance. Compared with dialogues with simulators, the average total rewards of dialogues with human descends less than 3 percent in both 2 and 3 slots tasks.