Mix-Initiative Response Generation with Dynamic Prefix Tuning
This work addresses the cross-contamination issue in dialogue systems for users needing more natural and controlled conversations, representing an incremental improvement through a novel tuning method.
The paper tackled the problem of dialogue systems confusing different conversational initiatives, which leads to inappropriate responses, by proposing a mix-initiative dynamic prefix tuning framework that decouples initiative factors and dynamically adjusts them during generation. The result showed that this framework outperformed previous baselines on automatic metrics and human evaluations across two public datasets, and successfully generated responses with manipulated initiatives.
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.