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- W4320519918 abstract "Semantic search for candidate retrieval is an important yet neglected problem in retrieval-based Chatbots, which aims to select a bunch of candidate responses efficiently from a large pool. The existing bottleneck is to ensure the model architecture having two points: 1) rich interactions between a query and a response to produce query-relevant responses; 2) ability of separately projecting the query and the response into latent spaces to apply efficiently in semantic search during online inference. To tackle this problem, we propose a novel approach, called Multitask-based Semantic Search Neural Network (MSSNN) for candidate retrieval, which accomplishes query-response interactions through multi-tasks. The method employs a Seq2Seq modeling task to learn a good query encoder, 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." @default.
- W4320519918 created "2023-02-14" @default.
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- W4320519918 date "2022-08-23" @default.
- W4320519918 modified "2023-10-18" @default.
- W4320519918 title "Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval" @default.
- W4320519918 doi "https://doi.org/10.48550/arxiv.2208.11018" @default.
- W4320519918 hasPublicationYear "2022" @default.
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