{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:01:43Z","timestamp":1743022903572,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819770069"},{"type":"electronic","value":"9789819770076"}],"license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-7007-6_9","type":"book-chapter","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:01:43Z","timestamp":1726941703000},"page":"116-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Dialogue State Tracking with\u00a0Interactive Acts Attention and\u00a0Attention Divergence Loss Function"],"prefix":"10.1007","author":[{"given":"Jingjing","family":"Lan","sequence":"first","affiliation":[]},{"given":"Rongjiao","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Hai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chaobo","family":"He","sequence":"additional","affiliation":[]},{"given":"Tianyong","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"unstructured":"Wang, Q., Cao, Y., Li, P., Fu, Y., Lin, Z., Guo, L.: Slot dependency modeling for zero-shot cross-domain dialogue state tracking. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 510\u2013520 (2022)","key":"9_CR1"},{"doi-asserted-by":"crossref","unstructured":"Chen, G., Xu, Q., Zhan, C., Wang, F.L., Liu, K., Liu, H., Hao, T.: Improving open intent detection via triplet-contrastive learning and adaptive boundary. IEEE Trans. Consumer Electron. (2024)","key":"9_CR2","DOI":"10.1109\/TCE.2024.3363896"},{"doi-asserted-by":"crossref","unstructured":"Mo, D., et al.: SCLert: a span-based joint model for measurable quantitative information extraction from Chinese texts. IEEE Trans. Consumer Electron. (2023)","key":"9_CR3","DOI":"10.1109\/TCE.2023.3327681"},{"doi-asserted-by":"crossref","unstructured":"Lee, H., Lee, J., Kim, T.Y.: SUMBT: slot-utterance matching for universal and scalable belief tracking. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5478\u20135483 (2019)","key":"9_CR4","DOI":"10.18653\/v1\/P19-1546"},{"doi-asserted-by":"crossref","unstructured":"Rastogi, A., Zang, X., Sunkara, S., Gupta, R., Khaitan, P.: Towards scalable multi-domain conversational agents: the schema-guided dialogue dataset. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 8689\u20138696 (2020)","key":"9_CR5","DOI":"10.1609\/aaai.v34i05.6394"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1109\/TASLP.2021.3054309","volume":"29","author":"V Balaraman","year":"2021","unstructured":"Balaraman, V., Magnini, B.: Domain-aware dialogue state tracker for multi-domain dialogue systems. IEEE\/ACM Trans. Audio, Speech Lang. Process. 29, 866\u2013873 (2021)","journal-title":"IEEE\/ACM Trans. Audio, Speech Lang. Process."},{"doi-asserted-by":"crossref","unstructured":"Lee, C.H., Cheng, H., Ostendorf, M.: Dialogue state tracking with a language model using schema-driven prompting. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 4937\u20134949 (2021)","key":"9_CR7","DOI":"10.18653\/v1\/2021.emnlp-main.404"},{"unstructured":"Victor, Z., Caiming, X., Richard, S.: Global-locally self-attentive dialogue state tracker (2018)","key":"9_CR8"},{"doi-asserted-by":"crossref","unstructured":"Su, R., Wu, T.W., Juang, B.H.: Act-aware slot-value predicting in multi-domain dialogue state tracking. arXiv preprint arXiv:2208.02462 (2022)","key":"9_CR9","DOI":"10.21437\/Interspeech.2021-138"},{"doi-asserted-by":"crossref","unstructured":"Yang, L., Li, J., Li, S., Shinozaki, T.: Multi-domain dialogue state tracking with disentangled domain-slot attention. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 4928\u20134938 (2023)","key":"9_CR10","DOI":"10.18653\/v1\/2023.findings-acl.304"},{"doi-asserted-by":"crossref","unstructured":"Jacqmin, L., Barahona, L.M.R., Favre, B.: \u201cdo you follow me?\u201d: a survey of recent approaches in dialogue state tracking. In: Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 336\u2013350 (2022)","key":"9_CR11","DOI":"10.18653\/v1\/2022.sigdial-1.33"},{"doi-asserted-by":"crossref","unstructured":"Xie, H., et al.: Correctable-DST: mitigating historical context mismatch between training and inference for improved dialogue state tracking. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 876\u2013889 (2022)","key":"9_CR12","DOI":"10.18653\/v1\/2022.emnlp-main.56"},{"doi-asserted-by":"crossref","unstructured":"Guo, J., Shuang, K., Zhang, K., Liu, Y., Li, J., Wang, Z.: Learning to imagine: distillation-based interactive context exploitation for dialogue state tracking. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 12845\u201312853 (2023)","key":"9_CR13","DOI":"10.1609\/aaai.v37i11.26510"},{"doi-asserted-by":"crossref","unstructured":"Guo, J., Shuang, K., Li, J., Wang, Z., Liu, Y.: Beyond the granularity: Multi-perspective dialogue collaborative selection for dialogue state tracking. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2320\u20132332 (2022)","key":"9_CR14","DOI":"10.18653\/v1\/2022.acl-long.165"},{"doi-asserted-by":"publisher","unstructured":"Searle, J.R., Vanderveken, D.: Speech acts and illocutionary logic. In: Vanderveken, D. (eds) Logic, Thought and Action, Logic, Epistemology, and the Unity of Science, vol. 8, pp. 109\u2013132 (2005). https:\/\/doi.org\/10.1007\/1-4020-3167-X_5","key":"9_CR15","DOI":"10.1007\/1-4020-3167-X_5"},{"doi-asserted-by":"crossref","unstructured":"Yu, D., Yu, Z.: MIDAS: a dialog act annotation scheme for open domain human machine spoken conversations. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 1103\u20131120 (2021)","key":"9_CR16","DOI":"10.18653\/v1\/2021.eacl-main.94"},{"unstructured":"Goel, R., et al.: Flexible and scalable state tracking framework for goal-oriented dialogue systems (2018)","key":"9_CR17"},{"doi-asserted-by":"crossref","unstructured":"Goel, R., Paul, S., Hakkani-T\u00fcr, D.: HyST: a hybrid approach for flexible and accurate dialogue state tracking. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol.\u00a02019, pp. 1458\u20131462 (2019)","key":"9_CR18","DOI":"10.21437\/Interspeech.2019-1863"},{"doi-asserted-by":"crossref","unstructured":"Lai, C.M., Hsu, M.H., Huang, C.W., Chen, Y.N.: Controllable user dialogue act augmentation for dialogue state tracking. In: Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 53\u201361 (2022)","key":"9_CR19","DOI":"10.18653\/v1\/2022.sigdial-1.5"},{"doi-asserted-by":"crossref","unstructured":"Ilic, S., Marrese-Taylor, E., Balazs, J., Matsuo, Y.: Deep contextualized word representations for detecting sarcasm and irony. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 2\u20137 (2018)","key":"9_CR20","DOI":"10.18653\/v1\/W18-6202"},{"unstructured":"Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. In: International Conference on Learning Representations (2016)","key":"9_CR21"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhao, J., Bao, J., Duan, C., Wu, Y., He, X.: LUNA: learning slot-turn alignment for dialogue state tracking. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3319\u20133328 (2022)","key":"9_CR22","DOI":"10.18653\/v1\/2022.naacl-main.242"},{"doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), p.\u00a01724. Association for Computational Linguistics (2014)","key":"9_CR23","DOI":"10.3115\/v1\/D14-1179"},{"doi-asserted-by":"crossref","unstructured":"Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: RACE: large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 785\u2013794 (2017)","key":"9_CR24","DOI":"10.18653\/v1\/D17-1082"},{"unstructured":"Eric, M., et al.: MultiWOZ 2.1: a consolidated multi-domain dialogue dataset with state corrections and state tracking baselines. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 422\u2013428 (2020)","key":"9_CR25"},{"doi-asserted-by":"crossref","unstructured":"Zang, X., Rastogi, A., Sunkara, S., Gupta, R., Zhang, J., Chen, J.: MultiWOZ 2.2: a dialogue dataset with additional annotation corrections and state tracking baselines. In: Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, pp. 109\u2013117 (2020)","key":"9_CR26","DOI":"10.18653\/v1\/2020.nlp4convai-1.13"},{"doi-asserted-by":"crossref","unstructured":"Wu, C.S., Madotto, A., Hosseini-Asl, E., Xiong, C., Socher, R., Fung, P.: Transferable multi-domain state generator for task-oriented dialogue systems. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 808\u2013819 (2019)","key":"9_CR27","DOI":"10.18653\/v1\/P19-1078"},{"doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, R., Mao, Y., Xu, J.: Parallel interactive networks for multi-domain dialogue state generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1921\u20131931 (2020)","key":"9_CR28","DOI":"10.18653\/v1\/2020.emnlp-main.151"},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Find or classify? Dual strategy for slot-value predictions on multi-domain dialog state tracking. In: Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pp. 154\u2013167 (2020)","key":"9_CR29","DOI":"10.18653\/v1\/2020.emnlp-main.243"},{"unstructured":"Zhou, L., Small, K.: Multi-domain dialogue state tracking as dynamic knowledge graph enhanced question answering. arXiv preprint arXiv:1911.06192 (2019)","key":"9_CR30"},{"doi-asserted-by":"crossref","unstructured":"Heck, M., et al.: Trippy: a triple copy strategy for value independent neural dialog state tracking. In: Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 35\u201344 (2020)","key":"9_CR31","DOI":"10.18653\/v1\/2020.sigdial-1.4"},{"doi-asserted-by":"crossref","unstructured":"Shin, J., Yu, H., Moon, H., Madotto, A., Park, J.: Dialogue summaries as dialogue states (ds2), template-guided summarization for few-shot dialogue state tracking. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 3824\u20133846 (2022)","key":"9_CR32","DOI":"10.18653\/v1\/2022.findings-acl.302"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7007-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:04:36Z","timestamp":1726941876000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7007-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"ISBN":["9789819770069","9789819770076"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7007-6_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024,9,22]]},"assertion":[{"value":"22 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aaci.org.hk\/ncaa2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}