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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\u2019s performance to that of 12 other models. Experiment results show that SAM achieves substantial improvements, with up to 1.5%\n            <jats:italic>R<\/jats:italic>\n            <jats:sub>10<\/jats:sub>\n            @1 on Ubuntu Dialogue Corpus V2, 0.5%\n            <jats:italic>R<\/jats:italic>\n            <jats:sub>10<\/jats:sub>\n            @1 on Douban Conversation Corpus, and 1.3%\n            <jats:italic>R<\/jats:italic>\n            <jats:sub>10<\/jats:sub>\n            @1 on E-commerce Corpus.\n          <\/jats:p>","DOI":"10.1145\/3545570","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T10:18:25Z","timestamp":1656584305000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["SAM: Multi-turn Response Selection Based on Semantic Awareness Matching"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1823-2726","authenticated-orcid":false,"given":"Rongjunchen","family":"Zhang","sequence":"first","affiliation":[{"name":"Swinburne University of Technology, Australia and CSIRO\u2019s Data61, Hawthorn, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0626-3576","authenticated-orcid":false,"given":"Tingmin","family":"Wu","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Australia and CSIRO\u2019s Data61, Hawthorn, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0655-666X","authenticated-orcid":false,"given":"Sheng","family":"Wen","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Hawthorn, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3289-6599","authenticated-orcid":false,"given":"Surya","family":"Nepal","sequence":"additional","affiliation":[{"name":"CSIRO\u2019s Data61, Marsfield, New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-0176","authenticated-orcid":false,"given":"Cecile","family":"Paris","sequence":"additional","affiliation":[{"name":"CSIRO\u2019s Data61, Marsfield, New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5252-0831","authenticated-orcid":false,"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Hawthorn, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","article-title":"Standardized usability questionnaires: Features and quality focus","volume":"6","author":"Assila Ahlem","year":"2016","unstructured":"Ahlem Assila, Houcine Ezzedine, et\u00a0al. 2016. 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