{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T13:07:45Z","timestamp":1779282465114,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032212887","type":"print"},{"value":"9783032212894","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-21289-4_22","type":"book-chapter","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:04:23Z","timestamp":1774314263000},"page":"336-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ExpertMix: Aspect and\u00a0Severity Detection in\u00a0Conversational Complaints"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6608-0400","authenticated-orcid":false,"given":"Sarmistha","family":"Das","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-4751","authenticated-orcid":false,"given":"Apoorva","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2918-7634","authenticated-orcid":false,"given":"Rishu Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2181-044X","authenticated-orcid":false,"given":"Navneet","family":"Shreya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5458-9381","authenticated-orcid":false,"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"22_CR1","unstructured":"Llama3-2b-base. https:\/\/huggingface.co\/andrijdavid\/Llama3-2B-Base (2024), hugging Face model repository"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Chao, G.L., Lane, I.: Bert-DST: scalable end-to-end dialogue state tracking with bidirectional encoder representations from transformer. arXiv preprint arXiv:1907.03040 (2019)","DOI":"10.21437\/Interspeech.2019-1355"},{"key":"22_CR3","unstructured":"Chen, T., Zhang, R., Hinton, G.: Analog bits: generating discrete data using diffusion models with self-conditioning. arXiv preprint arXiv:2208.04202 (2022)"},{"key":"22_CR4","unstructured":"Chen, X., Wakatsuki, T., Sawada, K.: rinna\/gemma-2-baku-2b-it, https:\/\/huggingface.co\/rinna\/gemma-2-baku-2b-it"},{"issue":"4","key":"22_CR5","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/J.DSS.2007.10.010","volume":"44","author":"K Coussement","year":"2008","unstructured":"Coussement, K., den Poel, D.V.: Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decis. Support Syst. 44(4), 870\u2013882 (2008). https:\/\/doi.org\/10.1016\/J.DSS.2007.10.010","journal-title":"Decis. Support Syst."},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Das, S., Mujavarsheik, B., Lyngkhoi, R.Z., Saha, S., Maurya, A.: Deciphering the complaint aspects: towards an aspect-based complaint identification model with video complaint dataset in finance. In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 7195\u20137204. IEEE (2025)","DOI":"10.1109\/WACV61041.2025.00699"},{"issue":"3","key":"22_CR7","doi-asserted-by":"publisher","first-page":"3606","DOI":"10.1109\/TCSS.2023.3338357","volume":"11","author":"S Das","year":"2024","unstructured":"Das, S., Singh, A., Saha, S., Maurya, A.: Negative review or complaint? exploring interpretability in financial complaints. IEEE Trans. Comput. Soc. Syst. 11(3), 3606\u20133615 (2024)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"22_CR8","doi-asserted-by":"publisher","unstructured":"Devanathan, R., Singh, A., Poornash, A.S., Saha, S.: Seeing beyond words: multimodal aspect-level complaint detection in ecommerce videos. In: Cai, J., et al., (eds.) Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024 - 1 November 2024, pp. 243\u2013252. ACM (2024). https:\/\/doi.org\/10.1145\/3664647.3681595","DOI":"10.1145\/3664647.3681595"},{"key":"22_CR9","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, June 2-7, 2019, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"22_CR10","unstructured":"Du, N., et\u00a0al.: Glam: efficient scaling of language models with mixture-of-experts. In: International Conference on Machine Learning, pp. 5547\u20135569. PMLR (2022)"},{"key":"22_CR11","unstructured":"Freund, Y., Schapire, R.E., et\u00a0al.: Experiments with a new boosting algorithm. In: ICML. vol.\u00a096, pp. 148\u2013156. Citeseer (1996)"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Gao, X., Zhang, Y., Galley, M., Brockett, C., Dolan, B.: Dialogue response ranking training with large-scale human feedback data. arXiv preprint arXiv:2009.06978 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.28"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Hardalov, M., Koychev, I., Nakov, P.: Towards automated customer support. In: Artificial Intelligence: Methodology, Systems, and Applications: 18th International Conference, AIMSA 2018, Varna, September 12\u201314, 2018, Proceedings 18, pp. 48\u201359. Springer (2018)","DOI":"10.1007\/978-3-319-99344-7_5"},{"key":"22_CR14","unstructured":"He, J., Qiu, J., Zeng, A., Yang, Z., Zhai, J., Tang, J.: Fastmoe: a fast mixture-of-expert training system. arXiv preprint arXiv:2103.13262 (2021)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Hwang, J.D., et al.: Comet-atomic 2020: on symbolic and neural commonsense knowledge graphs. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i7.16792"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Jin, M., Aletras, N.: Modeling the severity of complaints in social media. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pp. 2264\u20132274. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.180","DOI":"10.18653\/v1\/2021.naacl-main.180"},{"key":"22_CR17","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Li, J., Su, Q., Yang, Y., Jiang, Y., Wang, C., Xu, H.: Adaptive gating in mixture-of-experts based language models. arXiv preprint arXiv:2310.07188 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.217"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Ni, J., Abrego, G.H., Constant, N., Ma, J., Hall, K.B., Cer, D., Yang, Y.: Sentence-t5: scalable sentence encoders from pre-trained text-to-text models. arXiv preprint arXiv:2108.08877 (2021)","DOI":"10.18653\/v1\/2022.findings-acl.146"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Preotiuc-Pietro, D., Gaman, M., Aletras, N.: Automatically identifying complaints in social media. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, July 28- August 2, 2019, Volume 1: Long Papers, pp. 5008\u20135019. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1495"},{"issue":"140","key":"22_CR21","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"22_CR22","unstructured":"Shen, S., et\u00a0al.: Mixture-of-experts meets instruction tuning: a winning combination for large language models. arXiv preprint arXiv:2305.14705 (2023)"},{"issue":"1","key":"22_CR23","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/TCSS.2022.3215528","volume":"11","author":"A Singh","year":"2024","unstructured":"Singh, A., Bhatia, R., Saha, S.: Complaint and severity identification from online financial content. IEEE Trans. Comput. Soc. Syst. 11(1), 660\u2013670 (2024). https:\/\/doi.org\/10.1109\/TCSS.2022.3215528","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Singh, A., Dey, S., Singha, A., Saha, S.: Sentiment and emotion-aware multi-modal complaint identification. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 12163\u201312171. AAAI Press (2022). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/21476","DOI":"10.1609\/aaai.v36i11.21476"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Singh, A., Gangwar, V., Sharma, S., Saha, S.: Knowing what and how: a multi-modal aspect-based framework for complaint detection. In: Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, April 2\u20136, 2023, Proceedings, Part II, pp. 125\u2013140. Springer (2023)","DOI":"10.1007\/978-3-031-28238-6_9"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Singh, A., Nazir, A., Saha, S.: Adversarial multi-task model for emotion, sentiment, and sarcasm aided complaint detection. In: Advances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10-14, 2022, Proceedings, Part I. Lecture Notes in Computer Science, vol. 13185, pp. 428\u2013442. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-030-99736-6_29","DOI":"10.1007\/978-3-030-99736-6_29"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Singh, A., Saha, S., Hasanuzzaman, M., Dey, K.: Multitask learning for complaint identification and sentiment analysis. Cogn. Comput., 1\u201316 (2021)","DOI":"10.1007\/s12559-021-09844-7"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Singh, A., Verma, A., Jain, R., Saha, S.: Investigating the impact of multimodality and external knowledge in aspect-level complaint and sentiment analysis. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 2291\u20132300 (2023)","DOI":"10.1145\/3583780.3614937"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Singh, R.K., Shreya, N., Das, S., Singh, A., Saha, S.: Talk, snap, complain: validation-aware multimodal expert framework for fine-grained customer grievances. arXiv preprint arXiv:2511.14693 (2025)","DOI":"10.1609\/aaai.v40i46.41275"},{"key":"22_CR30","unstructured":"Team, G., et\u00a0al.: Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)"},{"key":"22_CR31","unstructured":"Team, G., et\u00a0al.: Gemma 2: improving open language models at a practical size. arXiv preprint arXiv:2408.00118 (2024)"},{"key":"22_CR32","unstructured":"Wang, P., et al.: Qwen2-VL: enhancing vision-language model\u2019s perception of the world at any resolution. arXiv preprint arXiv:2409.12191 (2024)"},{"key":"22_CR33","unstructured":"Yang, Y., Tang, Y., Tam, K.Y.: Investlm: a large language model for investment using financial domain instruction tuning. arXiv preprint arXiv:2309.13064 (2023)"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Dialogpt: large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536 (2019)","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Zhong, M., Liu, Y., Xu, Y., Zhu, C., Zeng, M.: Dialoglm: pre-trained model for long dialogue understanding and summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a036, pp. 11765\u201311773 (2022)","DOI":"10.1609\/aaai.v36i10.21432"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21289-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:12:45Z","timestamp":1779279165000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21289-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032212887","9783032212894"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21289-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"25 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delft","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2026.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}