Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
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.
Code (1)
Tasks
Response GenerationRetrievalSimilar Papers 제목 키워드 기반
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
Knowledge retrieval is one of the major challenges in building a knowledge-grounded dialogue system. A common method is to use a neural retriever with a distributed approximate nearest-neighbor database to quickly find t…
Language ModelingLanguage ModellingLarge Language ModelResponse Generation+1Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations
Grounding dialogue agents with knowledge documents has sparked increased attention in both academia and industry. Recently, a growing body of work is trying to build retrieval-based knowledge-grounded dialogue systems. W…
RetrievalA Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations
Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e.g., documents) when conversing with humans. However, it is non-trivial t…
Language ModellingRetrievalTask 2Getting Better Dialogue Context for Knowledge Identification by Leveraging Document-level Topic Shift
To build a goal-oriented dialogue system that can generate responses given a knowledge base, identifying the relevant pieces of information to be grounded in is vital. When the number of documents in the knowledge base i…
RetrievalPK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue
Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation. However, each grounding has been mostly researched in isolation with more practical multi-context…
Data AugmentationResponse GenerationRetrieval