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