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Internet Technol."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on\n            <jats:bold>natural language understanding<\/jats:bold>\n            (\n            <jats:bold>NLU<\/jats:bold>\n            ) technologies. As one core task in NLU,\n            <jats:bold>sentence semantic matching<\/jats:bold>\n            (\n            <jats:bold>SSM<\/jats:bold>\n            ) has widely existed in various interaction scenarios. Recently, deep learning\u2013based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as\n            <jats:bold>one-dimensional<\/jats:bold>\n            (\n            <jats:bold>1D<\/jats:bold>\n            ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel\n            <jats:bold>3D CNN-based SSM<\/jats:bold>\n            (\n            <jats:bold>3DSSM<\/jats:bold>\n            ) method for human\u2013robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are concatenated together to feed into another 3D CNN to capture the interactive features between them to generate the final matching representation. Finally, the semantic matching degree is judged with the sigmoid function by taking the learned matching representation as the input. Extensive experiments on two real-world datasets demonstrate that 3DSSM is able to achieve comparable or even better performance over the state-of-the-art competing methods.\n          <\/jats:p>","DOI":"10.1145\/3450520","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T15:01:57Z","timestamp":1626447717000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Sentence Semantic Matching Based on 3D CNN for Human\u2013Robot Language Interaction"],"prefix":"10.1145","volume":"21","author":[{"given":"Wenpeng","family":"Lu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoujin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Can","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Gold Coast, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Jian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Zhongguancun, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Zhongguancun, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00256"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367722.3367992"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219928"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1536"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.547"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50496-4_71"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107539"},{"key":"e_1_2_1_9_1","volume-title":"Hierarchical deep CNN feature set-based representation learning for robust cross-resolution face recognition","author":"Gao Guangwei","year":"2020","unstructured":"Guangwei Gao , Yi Yu , Jian Yang , Guo-Jun Qi , and Meng Yang . 2020. 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