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Semantic search for candidate retrieval is an important yet neglected problemin retrieval-based Chatbots, which aims to select a bunch of candidateresponses efficiently from a large pool. The existing bottleneck is to ensurethe model architecture having two points: 1) rich interactions between a queryand a response to produce query-relevant responses; 2) ability of separatelyprojecting the query and the response into latent spaces to apply efficientlyin semantic search during online inference. To tackle this problem, we proposea novel approach, called Multitask-based Semantic Search Neural Network (MSSNN)for candidate retrieval, which accomplishes query-response interactions throughmulti-tasks. The method employs a Seq2Seq modeling task to learn a good queryencoder, and then performs a word prediction task to build response embeddings,finally conducts a simple matching model to form the dot-product scorer.Experimental studies have demonstrated the potential of the proposed approach.

Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval