By how much does their model outperform the baseline?
Best accuracy result of proposed model is 82.73, 79.35 compared to best baseline result of 80.75, 78.68 on MultiWoz and DailyDialogue datasets respectively.
Producing natural and accurate responses like human beings is the ultimate goal of intelligent dialogue agents. So far, most of the past works concentrate on selecting or generating one pertinent and fluent response according to current query and its context. These models work on a one-to-one environment, making one response to one utterance each round. However, in real human-human conversations, human often sequentially sends several short messages for readability instead of a long message in one turn. Thus messages will not end with an explicit ending signal, which is crucial for agents to decide when to reply. So the first step for an intelligent dialogue agent is not replying but deciding if it should reply at the moment. To address this issue, in this paper, we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly. Our method has two imaginator modules and an arbitrator module. The two imaginators will learn the agent's and user's speaking style respectively, generate possible utterances as the input of the arbitrator, combining with dialogue history. And the arbitrator decides whether to wait or to make a response to the user directly. To verify the performance and effectiveness of our method, we prepared two dialogue datasets and compared our approach with several popular models. Experimental results show that our model performs well on addressing ending prediction issue and outperforms baseline models.
All species are unique, but languages make humans uniquest BIBREF0. Dialogues, especially spoken and written dialogues, are fundamental communication mechanisms for human beings. In real life, tons of businesses and entertainments are done via dialogues. This makes it significant and valuable to build an intelligent dialogue product. So far there are quite a few business applications of dialogue techniques, e.g. personal assistant, intelligent customer service and chitchat companion. The quality of response is always the most important metric for dialogue agent, targeted by most existing work and models searching the best response. Some works incorporate knowledge BIBREF1, BIBREF2 to improve the success rate of task-oriented dialogue models, while some others BIBREF3 solve the rare words problem and make response more fluent and informative. Despite the heated competition of models, however, the pace of interaction is also important for human-computer dialogue agent, which has drawn less or no attention. Figure FIGREF1 shows a typical dialogue fragment in an instant message program. A user is asking the service about the schedule of the theater. The user firstly says hello (U11) followed by demand description (U12), and then asks for suggested arrangement (U13), each of which is sent as a single message in one turn. The agent doesn't answer (A2) until the user finishes his description and throws his question. The user then makes a decision (U21) and asks a new question (U22). And then the agent replies with (A3). It's quite normal and natural that the user sends several messages in one turn and the agent waits until the user finished his last message, otherwise the pace of the conversation will be messed up. However, existing dialogue agents can not handle well when faced with this scenario and will reply to every utterance received immediately. There are two issues when applying existing dialogue agents to real life conversation. Firstly, when user sends a short utterance as the start of a conversation, the agent has to make a decision to avoid generating bad responses based on semantically incomplete utterance. Secondly, dialogue agent cutting in the conversation at an unreasonable time could confuse user and mess up the pace of conversation, leading to nonsense interactions. To address these two issues, in this paper, we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to recognize if it is the appropriate moment for agent to reply when agent receives a message from the user. In our method, we have two imaginator modules and an arbitrator module. Imaginators will learn both of the agent's and user's speaking styles respectively. The arbitrator will use the dialogue history and the imagined future utterances generated by the two imaginators to decide whether the agent should wait user or make a response directly. In summary, this paper makes the following contributions: We first addressed an interaction problem, whether the dialogue model should wait for the end of the utterance or make a response directly in order to simulate real life conversation and tried several popular baseline models to solve it. We proposed a novel Imagine-then-Arbitrate (ITA) neural dialogue model to solve the problem mentioned above, based on both of the historical conversation information and the predicted future possible utterances. We modified two popular dialogue datasets to simulate the real human dialogue interaction behavior. Experimental results demonstrate that our model performs well on addressing ending prediction issue and the proposed imaginator modules can significantly help arbitrator outperform baseline models.