Speaker or Listener? The Role of a Dialog Agent (2020)

Abstract: For decades, chitchat bots are designed as a listener to passively answer what people ask. This passive and relatively simple dialogue mechanism gains less attention from humans and consumes the interests of human beings rapidly. Therefore some recent researches attempt to endow the bots with proactivity through external knowledge to transform the role from a listener to a speaker with a hypothesis that the speaker expresses more just like a knowledge disseminator. However, along with the proactive manner introduced into a dialogue agent, an issue arises that, with too many knowledge facts to express, the agent starts to talks endlessly, and even completely ignores what the other expresses in dialogue sometimes, which greatly harms the interest of the other chatter to continue the conversation. To the end, we propose a novel model named Initiative-Imitate to interact with adaptive initiative throughout a dialogue. It forces the agent to express in parallel with the appropriate role during the whole conversation. The corresponding experiments show the proposed Initiative-Imitate obtains competitive results both on the automatic and manual metrics. And the fluency and engagement of the chatbot have also been improved significantly. Besides, the case study indicates the Initiative-Imitate can constantly transfer to appropriate role timely and response more properly during the whole continuous conversation.

## introduction automatic human-machine conversation lies in the core of artificial intelligence (ai) and natural language processing (nlp). many researchers have developed lots of dialogue systems, such as rulebased @xcite , retrieval-based @xcite and generationbased like neural networks @xcite @xcite . for decades, machine agent is generally presumed to be a passive role with the ability to answer what humans ask. however, this passive and relatively simple response mechanism consumes the interest of the other dialogue participant rapidly @xcite . actually in a continuous humanhuman conversation, both participants need to be a speaker to lead current topics. this phenomenon is statistically summarized from the analysis on a real human-human dataset named dailydialog @xcite as depicted in figure 1 . two humans take the topic leading role like a speaker to introduce something new in turns. thus in human-machine conversation, the dialogue agent side needs to act as a speaker timely and appropriately. ## related work in the last several years, neural conversational models become prevalent due to a new round of artificial intelligence. and usually, they provide better responses than early rule-based or template-based dialogue systems @xcite . before this paper, there are two dominated trends in human-machine conversation research: the passive and the proactive. actually the passive starts much earlier than the other. the passive models always attempt to answer what a human asks. among these models, @xcite trained end-to-end neural conversation models on massive data. @xcite proposed a maximum mutual information objective function to promote generating diverse responses. however, these dialogue systems often generate generic, safe, and inconsistent responses @xcite . this leads to the arising of the second research line of neural conversation models. utilizing knowledge in dialogue can generate diverse, engaging, meaningful and personalized responses in a way. @xcite fused knowledge encoder representation with encoder final state to initialize the initial decoder state by element-wise sum. @xcite fed knowledge representation at each decoder hidden state computation. @xcite encoded commonsense knowledge during searching and attended over retrieved sub-graph when generating words. @xcite introduced the persona-chat dataset with dialogue agents personalized and showed various baseline performance including some generation based models like seq2seq and profile memory. deepcopy @xcite applied the copy mechanism both on source words and knowledge facts words during the word generation period. this enhances the system's expressive ability especially in terms of the out-of-vocabulary words. @xcite applied knowlegepost @xcite to minimize the divergence between knowledge prior and knowledge posterior distribution to learn a better knowledge representation and concatenated the knowledge context vector with decoder feed. @xcite fused multi-source languages aligned to the target translation with child-sum tree-lstms @xcite , and we draw enlightenment from the child-sum tree-lstms and apply it into knowledge fusion as a completely novel knowledge fusion method. however, this doesn't resolve the current role-to-play problem during the response phase, that is whether the agent needs to be a listener or speaker at present. looking into child-sum tree-lstms, we have found that the forget gate controls the proportion of one source to forget. it's just like the behavior of the role. the speaker needs more knowledge to transmit relative knowledge to the other, so he needs to forget less knowledge. while the listener requires more forgetting to decrease the influence of the knowledge input. as a result, it seems the forget gate here is just a hidden variable which represents the role. so we keep an absolute role label (1: proactive, 0: passive) to supervise the forget gate and make the forget gate to supervise the knowledge fusion proportion. different from the knowledge models above, our initiative-imitate fuses knowledge with source context in a novel manner. and the utilized knowledge adaptively changes according to the predicted role at each turn in a multi-turn dialog. to the best of our knowledge, our initiative-imitate is the first chatbot which models the role initiative of the participants in a continuous dialogue. ## model in this section, we first set up the problem, then demonstrate the individual modules in the proposed model framework in figure 2 . ## experiments the experiment part consists of several procedures: dataset preparation to adapt our task, a brief baseline introduction, training setup, and the result comparison with analysis. ## conclusion in this paper, we first point out the knowledge overusing problem existing in current proactive knowledge dialogue models. meanwhile, if the bot always plays a single role (proactive or passive) in a dialogue, the other dialog partner loses his/her interest rapidly. to deal with all these arising issues, we propose to distinguish the dialogue role in the whole dialogue. and for this purpose, we have designed initiative-imitate to deal with the knowledge utilizing and role-to-play problem. with the forget gate in the knowledge fusion module, we predict the role to play. correspondingly, the role-to-play label supervises the forget gate on how much knowledge left at this turn of response generation. in this way, we deal with the two issues simultaneously. as for the dataset, we prepare our dataset with duconv because of the implicit role setting in the dialog. the automatic evaluation of the initiative-imitate on the prepared dataset shows the enhancement of the introduction of the adaptive role setting in humanmachine dialog on the gram level. for the human evaluation, we introduce a new metric engagement to measure the engagement degree of chatbot in dialogues. this metric reflects how much the dialogue agent cares about what the other has said, which can be a vital measurement of the quality of the human-machine conversation. after that, models are evaluated with three metrics including fluency, coherence, and engagement. and the final results prove the positive effectiveness of the role-control to the response generation. in summary, we take an initial and meaningful step on the role-to-play setting and proper knowledge utilizing. as for future work, we will still insist on working on better role-to-play modeling. what's more, currently many metrics are applied in the result evaluation because they are complementary and can only reflect one aspect of the characteristics of dialogue respectively. so one better and unified metric is in urgent need in the field of automatic conversation evaluation just like the main metric bleu in machine translation.