Mix-Initiative Response Generation with Dynamic Prefix Tuning (2024)

Abstract: Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holistic response generation model without any distinction among different initiatives. It leads to the cross-contamination problem, where the model confuses different initiatives and generates inappropriate responses. Moreover, obtaining plenty of human annotations for initiative labels can be expensive. To address this issue, we propose a general mix-Initiative Dynamic Prefix Tuning framework (IDPT) to decouple different initiatives from the generation model, which learns initiative-aware prefixes in both supervised and unsupervised settings. Specifically, IDPT decouples initiative factors into different prefix parameters and uses the attention mechanism to adjust the selection of initiatives in guiding generation dynamically. The prefix parameters can be tuned towards accurate initiative prediction as well as mix-initiative response generation. Extensive experiments on two public dialogue datasets show that the proposed IDPT outperforms previous baselines on both automatic metrics and human evaluations. It also manages to generate appropriate responses with manipulated initiatives.

## introduction dialogue systems crept into our lives in various ways, such as social chatbots @xcite , conversational recommenders @xcite , task-specific assistants @xcite and so on. mixed initiative plays an important role in forming responses in these systems. for example, in a dialogue session, the user and the system can take turns to lead the conversation. the user could lead the conversation by introducing new topics, providing new requirements or negating the suggestions, while the system would also take initiative by actively asking questions or recommending items to the user. however, current dialogue systems pay much attention to training a holistic response generation model, neglecting to distinguish responses in terms of initiative, which could lead to the crosscontamination problem @xcite . as shown in figure 1 , when the user says, "the song thousand sad reasons is pretty nice!", the system could merely follow the topic, hence an appropriate response would simply be: "i like it!". yet, the system would also take control of the dialogue and recommend a new item. then the response will change to something like: "then i'd recommend to you jacky cheung's amazing song, be torn with grief ". suppose we feed these to train a holistic response generation model without distinction, what we get will mostly likely be an 'averaged' response, which would largely affect the accuracy and flexibility of responses hence hurt human satisfaction. there are indeed ways to introduce these affecting factors into the response generation model. for example, in empathetic dialogue systems, varied emotions also result in different responses. hence, one possible solution is to directly adapt these empathetic response generation models such as @xcite @xcite . however, there are differences in nature between empathy and initiative, which makes the adaption not appropriate. firstly, emotional tone is relatively consistent between dialogue participants and the change of emotional tone is less frequent. secondly, for responses with different emotions, there are only limited variations of patterns in responses @xcite . but for responses with different initiatives, the responses vary largely. some researchers @xcite design various strategies to prompt large language models (llms) for initiative-aware response generation. however, this kind of method requires deep understanding of each specific dataset to design effective prompts, which is of heavy workload. another possible solution is to fine-tune separate pre-trained language models for each initiative. however, a well-performed response generation model requires a large number of responses with initiative labels, but obtaining these labels is costly and even impractical. moreover, it also takes up a large amount of disk space to save each fine-tuned model for each initiative. therefore, motivated by @xcite , we study a prefix-based method to inject these initiative factors into separate network parameters for better generality and disk efficiency. to this end, we propose mix-initiative dynamic prefix tuning (idpt), a general and disk-efficient model to decouple different initiatives from the response generation model. idpt can work in both supervised and unsupervised settings, with limited or no initiative labels in the response training data. specifically, idpt tends to push initiative factors into different prefix parameters, while leaves the main generation task to the pre-trained language model. in supervised setting (low-data) with initiative labels, it recognizes initiatives and applies hard attention over initiative prefixes for response generation. in unsupervised setting without labels, it applies soft attention to consider various initiative prefixes for response generation. through training, idpt learns to select the proper prefix parameters to combine with the pre-trained language model for mix-initiative response generation. to sum up, our contributions are as follows: ## conclusion in this research, we underscored the significance of considering initiative when generating responses in dialogue systems. to this end, we introduced the mix-initiative dynamic prefix tuning (idpt) model, which effectively incorporates initiative cues by segregating initiative elements into distinct prefix parameters. recognizing the challenges posed by the scarcity of labeled initiative conversation data, our model is adept at adapting to both supervised and unsupervised learning environments. comprehensive experiments on two widely-used dialogue datasets affirms the superior performance of the proposed idpt model as compared to a series of baselines. notably, our proposed idpt model consistently produces suitable responses for the conversation context even when initiatives are intentionally manipulated.