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• W4283764330 abstract "Multi-turn response selection is a key issue in retrieval-based chatbots and has attracted considerable attention in the NLP (Natural Language processing) field. So far, researchers have developed many solutions that can select appropriate responses for multi-turn conversations. However, these works are still suffering from the semantic mismatch problem when responses and context share similar words with different meanings. In this article, we propose a novel chatbot model based on Semantic Awareness Matching, called SAM. SAM can capture both similarity and semantic features in the context by a two-layer matching network. Appropriate responses are selected according to the matching probability made through the aggregation of the two feature types. In the evaluation, we pick 4 widely used datasets and compare SAM’s performance to that of 12 other models. Experiment results show that SAM achieves substantial improvements, with up to 1.5% R 10 @1 on Ubuntu Dialogue Corpus V2, 0.5% R 10 @1 on Douban Conversation Corpus, and 1.3% R 10 @1 on E-commerce Corpus." @default.
• W4283764330 created "2022-07-02" @default.
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• W4283764330 date "2023-02-28" @default.
• W4283764330 modified "2023-10-16" @default.
• W4283764330 title "SAM: Multi-turn Response Selection Based on Semantic Awareness Matching" @default.
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• W4283764330 doi "https://doi.org/10.1145/3545570" @default.
• W4283764330 hasPublicationYear "2023" @default.
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