  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.
