博碩士論文 111453002 詳細資訊




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姓名 林勇志(Yung-Chih Lin)  查詢紙本館藏   畢業系所 資訊管理學系在職專班
論文名稱 深度學習模型於客服對話文本多標籤分類之研究
(Research on Deep Learning Model for Multi-Label Classification of Customer Service Dialogue)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2029-6-25以後開放)
摘要(中) 隨著數位科技的快速發展,傳統的客服模式已經發生了巨大的改變。企業需要透過數據分析,從多種管道蒐集和洞察顧客資訊,以提供個人化的服務。然而疫情期間大量專業人力流失,企業面臨著業務推展的困境,亟需在短期內尋求有效的解決方案。但龐大的數據,包括客服語音資料在內,需要經過複雜的處理和轉化才能加以應用,傳統的人工標註和冗長的分類作業已無法滿足現實需求。為了因應這些挑戰,本研究利用深度學習技術,提出了一種基於BERT預訓練模型的創新深度學習模型RPC-BERT,通過融合自適應權重衰減、自適應學習率和自定義機率獎懲係數,有效地降低了多標籤分類中的類別不平衡問題。所提出的RPC優化矩陣能有效地應用於客服對話內容的多標籤分類,相較其他深度學習模型,在準確率、精確率、召回率及F1各項評估指標皆有更佳的表現。此外透過實際案例進行模型的可用性驗證,以RPC優化矩陣機制來進行客服對話文本多標籤的分類處理,其結果除了符合研究之結論外,確認可運用於企業日常實務作業中,並滿足降低人力成本,提升作業效率的需求。
摘要(英) In the wake of rapid digital technological advancements, traditional customer service paradigms have undergone substantial metamorphosis. Enterprises are now compelled to leverage data analytics to collate and derive insights from multifarious channels of customer information, with the aim of delivering personalized services. However, the pandemic period has precipitated a significant exodus of specialized human capital, presenting corporations with formidable challenges in business expansion. This exigency necessitates the expeditious identification of efficacious solutions. The voluminous data, inclusive of customer service voice data, demands intricate processing and transformation prior to application. Conventional manual annotation and protracted classification procedures have become inadequate in meeting contemporary demands. To address these challenges, this research harnesses deep learning technologies to propose an innovative deep learning model, RPC-BERT, predicated on the BERT pre-training model. Through the integration of adaptive weight decay, adaptive learning rate, and customized probability reward-penalty coefficients, the model effectively mitigates class imbalance issues in multi-label classification tasks. The proposed RPC optimization matrix demonstrates efficacious application in multi-label classification of customer service dialogue content. Compared to other deep learning models, it exhibits superior performance across various evaluation metrics, including accuracy, precision, recall, and F1 score. Furthermore, the model′s applicability is validated through practical case studies. The implementation of the RPC optimization matrix mechanism for multi-label classification of customer service dialogue texts not only corroborates the research conclusions but also confirms its viability for integration into daily enterprise operations. This approach satisfies the dual objectives of reducing human resource costs and enhancing operational efficiency.
關鍵字(中) ★ 多標籤分類
★ 深度學習
★ 獎懲矩陣
★ 客服對話文本
關鍵字(英) ★ multi-label classification
★ deep learning
★ reward-penalty matrix
★ customer service dialogue
論文目次 摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
1 第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 5
1.3 研究貢獻 7
1.4 論文流程與架構 8
2 第二章 文獻探討 10
2.1 多標籤分類應用 11
2.2 多標籤分類及深度模型技術 13
3 第三章 研究方法 18
3.1 資料轉換與前處理 19
3.2 模型架構 20
3.3 自適應機制與損失函式 22
4 第四章 實驗結果與分析 26
4.1 資料集與前置處理 26
4.2 比較模型 29
4.3 模型效能驗證 32
4.4 RPC Matrix 敏感性分析 36
4.5 參數設定探討 38
4.6 實驗總結 43
4.7 實例驗證 45
5 第五章 結論與未來研究方向 49
5.1 研究結論 49
5.2 研究限制 50
5.3 未來研究方向 50
6 參考文獻 52
參考文獻 [1] IATA, "Air passenger market analysis December 2020," https://www.iata.org/economicsair-passenger-market-analysis-december-2020/. Accessed: Mar. 17, 2024.
[2] IATA, "Air passenger market analysis December 2023". https://www.iata.org/en/iata-repository/publications/economic-reports/air-passenger-market-analysis-december-2023/. Accessed: Mar. 17, 2024.
[3] S. Hong, M. Savoie, S. Joiner, and T. Kincaid, "Analysis of airline employees′ perceptions of corporate preparedness for COVID-19 disruptions to airline operations," Transport Policy, vol. 119, pp. 45-55, 2022.
[4] J. Doe and J. Smith, "The impact of digital transformation on the retailing value chain," Journal of Business Research, vol. 125, pp. 10-20, 2023.
[5] A. Haleem, M. Javaid, M. Qadri, R. Singh, and R. Suman, "Artificial Intelligence (AI) applications for marketing: A literature-based study," International Journal of Intelligent Networks, 2022, vol. 3, pp. 119–132.
[6] H. Gil-Gomez, V. Guerola-Navarro, R. Oltra-Badenes, and J. A. Lozano-Quilis, "Customer relationship management: Digital transformation and sustainable business model innovation," Economic Research-Ekonomska Istraživanja, 2020, vol. 33, no. 1, pp. 2733–2750.
[7] E. Ernawati, S. Baharin, and F. Kasmin, "A review of data mining methods in RFM-based customer segmentation," Journal of Physics: Conference Series, 2021, vol. 1869, no. 1, p. 012085.
[8] Gartner, "Gartner predicts conversational AI will reduce contact center agent labor costs by $80 billion in 2026". https://www.gartner.com/en/newsroom/press-releases/2022-08-31-gartner-predicts-conversational-ai-will-reduce-contac. Accessed: Mar. 17, 2024.
[9] C. Bascur and C. Rusu, "Customer experience in retail: A systematic literature review," Applied sciences, vol. 10, no. 21, p. 7644, 2020.
[10] Gartner, "Gartner says conversational AI capabilities will help drive worldwide contact center market to 16% growth in 2023". https://www.gartner.com/en/newsroom/press-releases/2023-07-31-gartner-says-conversational-ai-capabilities-will-help-drive-worldwide-contact-center-market-to-16-percent-growth-in-2023. Accessed: Mar. 17, 2024.
[11] J. Berg, E. Buesing, P. Hurst, V. Lai, and S. Mukhopadhyay, "The state of customer care in 2022," McKinsey & Company". https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-customer-care-in-2022. Accessed: Mar. 17, 2024.
[12] S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, "Deep learning--based text classification: a comprehensive review," ACM computing surveys (CSUR), 2021, vol. 54, no. 3, pp. 1-40.
[13] M. Al-Ayyoub, H. Seelawi, M. Zaghlol, H. Al-Natsheh, S. Suileman, A. Fadel, R. Badawi, A. Morsy, I. Tuffaha and M. Alijarrah, "Overview of the mowjaz multi-topic labelling task," in 2021 12th International Conference on Information and Communication Systems (ICICS), 2021, pp. 502-508.
[14] H. Hardy, K. Baker, L. Devillers, L. Lamel, S. Rosset, T. Strzalkowski, and N. Webb, "Multi-layer dialogue annotation for automated multilingual customer service," in Proc. of the ISLE Workshop on Dialogue Tagging for Multimodal Human Computer Interaction, Edinburgh, Dec. 2002.
[15] X. Zhang, J. Chen, R. Zheng, L. Li, X. Wang, and S. Lei, "A Multi-level and Multi-label Annotation Strategy for User Questions in ICT Customer Service," in 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2020, vol. 1, pp. 410-415.
[16] Y. Liu, B. Cao, K. Ma, and J. Fan, "Improving the classification of call center service dialogue with key utterances," Wireless Networks, vol. 27, no. 5, pp. 3395-3406, 2021.
[17] K. Poczeta, M. Płaza, T. Michno, M. Krechowicz, and M. Zawadzki, "A multi-label text message classification method designed for applications in call/contact centre systems," Applied Soft Computing, vol. 145, p. 110562, 2023.
[18] C. Liu, P. Wang, J. Xu, Z. Li, and J. Ye, "Automatic dialogue summary generation for customer service," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1957-1965.
[19] H. Zhang, L. Xiao, W. Chen, Y. Wang, and Y. Jin, "Multi-task label embedding for text classification," arXiv preprint arXiv:1710.07210, 2017.
[20] T. Wu, R. Su, and B. Juang, "A label-aware BERT attention network for zero-shot multi-intent detection in spoken language understanding," in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021, pp. 4884-4896.
[21] I. Casanueva, I. Vulić, G. Spithourakis, and P. Budzianowski, "NLU++: A multi-label, slot-rich, generalisable dataset for natural language understanding in task-oriented dialogue," arXiv preprint arXiv:2204.13021, 2022.
[22] 李彥伯, "基於BERT實現動態主題與文本之多標籤分類," 國立暨南國際大學學位論文, 2023.
[23] 林立中, "探索多標籤分類的應用, " 國立臺灣大學學位論文, 2022.
[24] 蘇成恩, "以憂鬱症病患為對象之多標籤對話生成," 國立臺灣大學學位論文, 2022.
[25] 林英延, "多標籤分類方法應用於問答系統," 國立中興大學學位論文, 2022.
[26] H. Liang, X. Sun, Y. Sun, and Y. Gao, "Text feature extraction based on deep learning: a review," EURASIP journal on wireless communications and networking, vol. 2017, no. 1, pp. 1-12, 2017.
[27] F. Shiri, T. Perumal, N. Mustapha, and R. Mohamed, "A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU," arXiv preprint arXiv:2305.17473, 2023.
[28] H. Mohammed, E. Dogdu, A. Görür and R. Choupani, "Multi-label classification of text documents using deep learning," in 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 4681-4689.
[29] A. Al-Qerem, M. Raja, S. Taqatqa, and M. Sara, "Utilizing Deep Learning Models (RNN, LSTM, CNN-LSTM, and Bi-LSTM) for Arabic Text Classification," in Artificial Intelligence-Augmented Digital Twins: Transforming Industrial Operations for Innovation and Sustainability, Cham: Springer Nature Switzerland, pp. 287-301, 2024.
[30] P. Liu, X. Qiu, and X. Huang, "Recurrent neural network for text classification with multi-task learning," arXiv preprint arXiv:1605.05101, 2016.
[31] 郭建宏, "基於Bi-LSTM演算法加速中文自然語言語意分析系統, " 國立臺北科技大學學位論文, 2021.
[32] E. Ahmadzadeh, H. Kim, O. Jeong, N. Kim, and I. Moon, "A deep bidirectional LSTM-GRU network model for automated ciphertext classification," IEEE access, vol. 10, pp. 3228-3237, 2022.
[33] M. Zulqarnain, R. Ghazali, M. Ghouse, and M. Mushtaq, "Efficient processing of GRU based on word embedding for text classification," JOIV: International Journal on Informatics Visualization, vol. 3, no. 4, pp. 377-383, 2019.
[34] A. Shewalkar, "Graduate School," Doctoral dissertation, North Dakota State University, 2018.
[35] J. Devlin, M. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018.
[36] C. Xu, W. Zhou, T. Ge, F. Wei, and M. Zhou, "Bert-of-theseus: Compressing bert by progressive module replacing," arXiv preprint arXiv:2002.02925, 2020.
[37] P. Rust, J. Pfeiffer, I. Vulić, S. Ruder, and I. Gurevych, "How good is your tokenizer? on the monolingual performance of multilingual language models," arXiv preprint arXiv:2012.15613, 2020.
[38] S. Choo and W. Kim, "A study on the evaluation of tokenizer performance in natural language processing," Applied Artificial Intelligence, vol. 37, no. 1, p. 2175112, 2023.
[39] H. Yang, "Bert meets chinese word segmentation," arXiv preprint arXiv:1909.09292, 2019.
[40] Y. Lai, Y. Liu, Y. Feng, S. Huang, and D. Zhao, "Lattice-BERT: leveraging multi-granularity representations in Chinese pre-trained language models," arXiv preprint arXiv:2104.07204, 2021.
[41] Y. Tian, "Multi-label Text Classification Combining BERT and Bi-GRU Based on the Attention Mechanism," Journal of Network Intelligence, vol. 8, no. 1, pp. 168-180, 2023.
[42] A. Tarekegn, M. Giacobini, and K. Michalak, "A review of methods for imbalanced multi-label classification," Pattern Recognition, vol. 118, p. 107965, 2021.
[43] J. Johnson and T. Khoshgoftaar, "Survey on deep learning with class imbalance," Journal of Big Data, vol. 6, no. 1, pp. 1-54, 2019.
[44] Y. Yang, Y. Lin, H. Chu, and H. Lin, "Deep learning with a rethinking structure for multi-label classification," in Asian Conference on Machine Learning, 2019, pp. 125-140.
[45] A. Blanco, A. Casillas, A. Perez, and A. de Ilarraza, "Multi-label clinical document classification: Impact of label-density," Expert Systems with Applications, vol. 138, p. 112835, 2019.
[46] A. Pal, M. Selvakumar, and M. Sankarasubbu, "Multi-label text classification using attention-based graph neural network," arXiv preprint arXiv:2003.11644, 2020.
[47] Y. Hou, Y. Lai, Y. Wu, W. Che, and T. Liu, "Few-shot learning for multi-label intent detection," in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, no. 14, pp. 13036-13044.
[48] R. Wang and X. Dai, "Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2022, pp. 672-679.
[49] 林育任, "多標籤分類中對稀有標籤的閥值調整策略之討論,"國立臺灣大學學位論文, 2023.
[50] Y. Chen, Y. Chen, and C. Hsu, "G-TransRec: A Transformer-Based Next-Item Recommendation With Time Prediction," in IEEE Transactions on Computational Social Systems, 2024, pp. 4-5.
[51] B. Ghojogh and A. Ghodsi, "Attention mechanism, transformers, BERT, and GPT: Tutorial and Survey," 2020.
[52] K. Nakamura and B. W. Hong, "Adaptive weight decay for deep neural networks," IEEE Access, vol. 7, pp. 118857-118865, 2019.
[53] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer and V. Stoyanov, "Roberta: A robustly optimized bert pretraining approach," arXiv preprint arXiv:1907.11692, 2019.
[54] "CKIP Lab 中文詞知識庫小組, " 中央研究院. https://ckip.iis.sinica.edu.tw/project/ws. Accessed: Mar. 17, 2024.
[55] 吳澤鑫, "深度神經網路於中文斷詞之研究," 學位論文, 國立臺灣科技大學學位論文, 2023.
[56] 徐子杰, "基於Stacking與Transformer的中文斷詞模型之研究," 國立臺灣科技大學學位論文, 2023.
指導教授 陳以錚(Yi-Cheng Chen) 審核日期 2024-6-26
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