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

• URL: http://arxiv.org/abs/2208.11018v1
• Date: Tue, 23 Aug 2022 15:07:35 GMT
• Title: Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval
• Authors: Libin Shi, Kai Zhang, Wenge Rong
• Abstract summary: We propose a novel approach, called Multitask-based Semantic Search Neural Network (MSSNN) for candidate retrieval. 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.
• Score: 12.615150401073711
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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