Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of previous work assumes that the relevant knowledge is given as input or retrieved from a static pool of knowledge. However, this assumption violates the real-world situation, where knowledge is continually updated and a chatbot has to dynamically retrieve useful knowledge. We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation. As the core module, a query producer is used to generate queries from a dialogue context to interact with a search engine. We design a training algorithm using cheap noisy supervision for the query producer, where the signals are obtained by comparing retrieved articles with the next dialogue response. As the result, the query producer is adjusted without any human annotation of gold queries, making it easily transferable to other domains and search engines. Experiments show that our query producer can achieve R@1 and R@5 rates of 62.4% and 74.8% for retrieving gold knowledge, and the overall model generates better responses over strong knowledge-aided baselines using BART and other typical systems.
Code (1)
Tasks
ArticlesChatbotResponse GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models
Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users. Commonsense-augmented dialogue models have been proposed that aim to infer commonsense knowledge from di…
Response GenerationSpecificityLearning to Acquire Knowledge from a Search Engine for Dialogue Response Generation
Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of previous work assumes that the relevant know…
ArticlesChatbotResponse GenerationResponse Enhanced Semi-supervised Dialogue Query Generation
Leveraging vast and continually updated knowledge from the Internet has been considered an important ability for a dialogue system. Therefore, the dialogue query generation task is proposed for generating search queries …
Domain AdaptationRAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues
In real-world applications with Large Language Models (LLMs), external retrieval mechanisms - such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented Generation (RAG) - are often employed to …
RAGRetrievalRetrieval-augmented GenerationA Survey on Retrieval-Augmented Text Generation
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrieval-augmented text generation has remarkable ad…
Machine TranslationResponse GenerationRetrievalRetrieval-augmented Generation+3