Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
This addresses the inference efficiency issue for researchers and practitioners in open-domain chit-chat dialogue systems, though it is incremental as it builds on existing adapter methods.
The paper tackles the problem of inefficient inference in knowledge-grounded dialogue by proposing KnowExpert, a retrieval-free framework that uses adapters to inject knowledge into pre-trained language models, achieving comparable performance to retrieval-based methods with improved time efficiency.
To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. The existing methods tackle the knowledge grounding challenge by retrieving the relevant sentences over a large corpus and augmenting the dialogues with explicit extra information. Despite their success, however, the existing works have drawbacks in inference efficiency. This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task. To the best of our knowledge, this is the first attempt to tackle this challenge without retrieval in this task under an open-domain chit-chat scenario. The experimental results show that Knowexpert performs comparably with some retrieval-based baselines while being time-efficient in inference, demonstrating the effectiveness of our proposed method.