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題名 生命專線對談自適應重點萃取進行自殺意念分析
Self-Adapted Utterance Selection for Suicidal Ideation Analysis in Lifeline Conversations作者 王中伶
Wang, Zhong-Ling貢獻者 黃瀚萱
Huang, Hen-Hsen
王中伶
Wang, Zhong-Ling關鍵詞 生命專線
自殺意念偵測
自適應重點萃取
對話理解
自然語言處理
Lifeline
Suicidal Ideation Detection
Self-Adapted Utterance Selection
Conversation Understanding
Natural Language Processing日期 2022 上傳時間 2-Sep-2022 15:05:19 (UTC+8) 摘要 近年來，心理健康逐漸受到重視，尤其致命性的自殺議題更得到關注，臺灣安心專線針對該議題提供民眾免費撥打服務，透過通話方式給予來電者心理方面的建議及協助，本論文進而透過對談內容，進行自殺意念風險分析。本論文之資料集來自安心專線真實個案，由心理專業團隊聽寫成文本，再經專家依照個案的自殺意念狀況進行分類。由於社工與來電者的對談內容冗長又充滿雜訊，不利機器學習模型預測，因此，基於自然語言處理技術，本論文提出自適應萃取方法，將對談中擁有重要特徵及資訊的句子萃取出來並將其串接，再利用該縮減內容，預測自殺意念風險。實驗結果顯示，本方法於各風險類別得出最高效能，且被萃取出來的句子得以進行可解釋性的語意分析。此外，針對自殺防治，以提早偵測任務於各對談上進行測試，期望在對談中，能越早發現來電者的需求並及時給予適當的資源，降低社工的負擔。最後，除了自殺議題之外，我們希望將本方法廣泛應用至不同領域，達成重點內容萃取、資料長度縮減，進而提升效能且更有效率地進行語意分析，因此，以航空客服及電影影評資料集進行實驗，且驗證本方法適合的使用情境。
Our work investigates an important issue in mental healthcare, suicidal ideation detection in the phone-call conversations of Taiwan Lifeline. The conversation between the caller and the counsellor is often long, noisy, and covering diverse topics, making the model challenged to classify the suicidal ideation of the caller. To facilitate the NLP model for suicidal ideation detection, we propose a novel self-adapted approach that aims to select the critical utterances that are easier for the underlying NLP model to discriminate. The real-world Lifeline transcriptions labeled by experts are adopted in experiments. Experimental results show the effectiveness of our approach in overall performance improvement. The selected utterances can also be regarded as explanation information. The early detection is effective for our study of suicide prevention. Not limited to the healthcare domain, our approach is applied to the flight booking state classification on the AirDialogue dataset and sentiment binary classification on IMDb and Polarity datasets to explore the suitable scenario for general applications.參考文獻 [1] Sheri L. Johnson Ann M. Kring. Abnormal Psychology, chapter Mood Disorders. Wiley, 2017. ISBN 978-1-119-39523-2.[2] Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv:2004.05150, 2020.[3] Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, and Jia-Yu Pan. AirConcierge: Generating task-oriented dialogue via efficient large-scale knowledge retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 884–897, Online, November 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.79. URL https://aclanthology.org/2020.findings-emnlp.79.[4] Glen Coppersmith, Ryan Leary, Patrick Crutchley, and Alex Fine. Natural language processing of social media as screening for suicide risk. Biomedical informatics insights, 10:1178222618792860, 2018.[5] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-1423. URL https://aclanthology.org/N19-1423.[6] Shahla Farzana, Mina Valizadeh, and Natalie Parde. Modeling dialogue in conversational cognitive health screening interviews. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1167–1177, Marseille, France, May 2020. European Language Resources Association. ISBN 979-10-95546-34-4. URL https://aclanthology.org/2020.lrec-1.147.[7] Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. Knowledge-aware assessment of severity of suicide risk for early intervention. In The World Wide Web Conference, WWW ’19, page 514–525, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450366748. doi: 10.1145/3308558.3313698. URL https://doi.org/10.1145/3308558.3313698.[8] ”Jonathan Gratch, Ron Artstein, Gale Lucas, Giota Stratou, Stefan Scherer, Angela Nazarian, Rachel Wood, Jill Boberg, David Devault, Stacy Marsella, David Traum, Albert ””Skip”” Rizzo, and Louis-Philippe Morency”. The distress analysis inter- view corpus of human and computer interviews. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, may 2014. European Language Resources Association (ELRA). ISBN 978-2-9517408-8-4.[9] Nancy Green, Curry Guinn, and Ronnie Smith. Assisting social conversation be- tween persons with Alzheimer’s disease and their conversational partners. In Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies, pages 37–46, Montréal, Canada, June 2012. Association for Computational Linguistics. URL https://aclanthology.org/W12-2906.[10] Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, and Zi Huang. Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1):214–226, 2021. doi: 10. 1109/TCSS.2020.3021467.[11] Miaofeng Liu, Yan Song, Hongbin Zou, and Tong Zhang. Reinforced training data selection for domain adaptation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1957–1968, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1189. URL https://aclanthology.org/P19-1189.[12] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 142–150, Portland, Oregon, USA, June 2011. Association for Computational Linguistics. URL http://www.aclweb.org/anthology/ P11-1015.[13] Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, and Rajiv Ratn Shah. SNAP-BATNET: Cascading author profiling and social network graphs for suicide ideation detection on social media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 147–156, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-3019. URL https://www.aclweb.org/anthology/N19-3019.[14] Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pages 271–278, Barcelona, Spain, July 2004. doi: 10.3115/1218955.1218990. URL https://aclanthology.org/P04-1035.[15] Alex Rinaldi, Jean Fox Tree, and Snigdha Chaturvedi. Predicting depression in screening interviews from latent categorization of interview prompts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7–18, Online, July 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.2. URL https://aclanthology.org/2020.acl-main.2.[16] Ramit Sawhney, Harshit Joshi, Saumya Gandhi, and Rajiv Ratn Shah. Towards ordinal suicide ideation detection on social media. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pages 22–30, 2021.[17] Annika Marie Schoene, Alexander Turner, Geeth Ranmal De Mel, and Nina Dethlefs. Hierarchical multiscale recurrent neural networks for detecting suicide notes. IEEE Transactions on Affective Computing, pages 1–1, 2021. doi: 10.1109/TAFFC.2021. 3057105.[18] Faisal Muhammad Shah, Farsheed Haque, Ragib Un Nur, Shaeekh Al Jahan, and Zarar Mamud. A hybridized feature extraction approach to suicidal ideation detection from social media post. In 2020 IEEE Region 10 Symposium (TENSYMP), pages 985–988, 2020. doi: 10.1109/TENSYMP50017.2020.9230733.[19] Tan Thongtan and Tanasanee Phienthrakul. Sentiment classification using document embeddings trained with cosine similarity. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguistics: Student Research Workshop, pages 407–414, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-2057. URL https://aclanthology.org/P19-2057.[20] Wei Wei, Quoc Le, Andrew Dai, and Jia Li. AirDialogue: An environment for goal-oriented dialogue research. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3844–3854, Brussels, Belgium, October-November 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1419. URL https://aclanthology.org/D18-1419.[21] Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le. Unsupervised data augmentation for consistency training. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS’20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546.[22] Zhongzhi Xu, Yucan Xu, Florence Cheung, Mabel Cheng, Daniel Lung, Yik Wa Law, Byron Chiang, Qingpeng Zhang, and Paul S.F. Yip. Detecting suicide risk us- ing knowledge-aware natural language processing and counseling service data. Social Science Medicine, 283:114176, 2021. ISSN 0277-9536. doi: https://doi.org/10. 1016/j.socscimed.2021.114176. URL https://www.sciencedirect.com/science/ article/pii/S0277953621005086.[23] Hannah Yao, Sina Rashidian, Xinyu Dong, Hongyi Duanmu, Richard N Rosenthal, and Fusheng Wang. Detection of suicidality among opioid users on reddit: Machine learning–based approach. J Med Internet Res, 22(11):e15293, Nov 2020. ISSN 1438-8871. doi: 10.2196/15293. URL http://www.jmir.org/2020/11/e15293/.[24] Pingyue Zhang, Mengyue Wu, Heinrich Dinkel, and Kai Yu. Depa: Self-supervised audio embedding for depression detection. In Proceedings of the 29th ACM International Conference on Multimedia, pages 135–143, 2021. 描述 碩士
國立政治大學
資訊科學系
109753106資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109753106 資料類型 thesis dc.contributor.advisor 黃瀚萱 zh_TW dc.contributor.advisor Huang, Hen-Hsen en_US dc.contributor.author (Authors) 王中伶 zh_TW dc.contributor.author (Authors) Wang, Zhong-Ling en_US dc.creator (作者) 王中伶 zh_TW dc.creator (作者) Wang, Zhong-Ling en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-Sep-2022 15:05:19 (UTC+8) - dc.date.available 2-Sep-2022 15:05:19 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2022 15:05:19 (UTC+8) - dc.identifier (Other Identifiers) G0109753106 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141640 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 109753106 zh_TW dc.description.abstract (摘要) 近年來，心理健康逐漸受到重視，尤其致命性的自殺議題更得到關注，臺灣安心專線針對該議題提供民眾免費撥打服務，透過通話方式給予來電者心理方面的建議及協助，本論文進而透過對談內容，進行自殺意念風險分析。本論文之資料集來自安心專線真實個案，由心理專業團隊聽寫成文本，再經專家依照個案的自殺意念狀況進行分類。由於社工與來電者的對談內容冗長又充滿雜訊，不利機器學習模型預測，因此，基於自然語言處理技術，本論文提出自適應萃取方法，將對談中擁有重要特徵及資訊的句子萃取出來並將其串接，再利用該縮減內容，預測自殺意念風險。實驗結果顯示，本方法於各風險類別得出最高效能，且被萃取出來的句子得以進行可解釋性的語意分析。此外，針對自殺防治，以提早偵測任務於各對談上進行測試，期望在對談中，能越早發現來電者的需求並及時給予適當的資源，降低社工的負擔。最後，除了自殺議題之外，我們希望將本方法廣泛應用至不同領域，達成重點內容萃取、資料長度縮減，進而提升效能且更有效率地進行語意分析，因此，以航空客服及電影影評資料集進行實驗，且驗證本方法適合的使用情境。 zh_TW dc.description.abstract (摘要) Our work investigates an important issue in mental healthcare, suicidal ideation detection in the phone-call conversations of Taiwan Lifeline. The conversation between the caller and the counsellor is often long, noisy, and covering diverse topics, making the model challenged to classify the suicidal ideation of the caller. To facilitate the NLP model for suicidal ideation detection, we propose a novel self-adapted approach that aims to select the critical utterances that are easier for the underlying NLP model to discriminate. The real-world Lifeline transcriptions labeled by experts are adopted in experiments. Experimental results show the effectiveness of our approach in overall performance improvement. The selected utterances can also be regarded as explanation information. The early detection is effective for our study of suicide prevention. Not limited to the healthcare domain, our approach is applied to the flight booking state classification on the AirDialogue dataset and sentiment binary classification on IMDb and Polarity datasets to explore the suitable scenario for general applications. en_US dc.description.tableofcontents 誌謝 i摘要 iiAbstract iiiContents ivList of Figures viiList of Tables viiiChapter 1 Introduction 11.1 Background 11.2 Motivation 31.3 Objectives 4Chapter 2 Related Work 62.1 Suicidal Ideation Detection 62.2 Conversation-based of Mental Disease Detection 8Chapter 3 Dataset 103.1 Data Storage 103.2 Suicidal Ideation Risk Definition 113.3 The Split of Dataset 12Chapter 4 Methodology 134.1 Overview 134.2 Task Definition 144.3 Self-Adapted Utterance Selection 154.4 Suicidal Ideation at the Utterance Level 164.5 Training of the Whole Framework 164.6 Inference 174.7 Dataflow 18Chapter 5 Experiments 205.1 Our Method 205.2 Vanilla BERT/Longformer Model 225.3 Multi-turn GRU Model 225.4 JLPC Model 225.5 LDA-based Utterance Selection Method 235.6 Reinforcement Learning Model 235.7 LIWC-based Method 25Chapter 6 Results and Discussion 266.1 Results 266.2 Choice of the Context Window 276.3 Caller versus Counsellor 286.4 Each Procedure of the Data Pre-processing 296.5 The Length of Condensed Conversations 316.6 Choice of the Input Length 33Chapter 7 Analysis 357.1 Semantic Analysis 357.2 The Content Selected by Different Methods 367.3 Early Detection Analysis 38Chapter 8 Applications 408.1 AirDialogue Dataset 408.2 IMDb Dataset 428.3 Polarity Dataset 44Chapter 9 Conclusion 48References 50 zh_TW dc.format.extent 1936910 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109753106 en_US dc.subject (關鍵詞) 生命專線 zh_TW dc.subject (關鍵詞) 自殺意念偵測 zh_TW dc.subject (關鍵詞) 自適應重點萃取 zh_TW dc.subject (關鍵詞) 對話理解 zh_TW dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) Lifeline en_US dc.subject (關鍵詞) Suicidal Ideation Detection en_US dc.subject (關鍵詞) Self-Adapted Utterance Selection en_US dc.subject (關鍵詞) Conversation Understanding en_US dc.subject (關鍵詞) Natural Language Processing en_US dc.title (題名) 生命專線對談自適應重點萃取進行自殺意念分析 zh_TW dc.title (題名) Self-Adapted Utterance Selection for Suicidal Ideation Analysis in Lifeline Conversations en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Sheri L. Johnson Ann M. Kring. Abnormal Psychology, chapter Mood Disorders. Wiley, 2017. ISBN 978-1-119-39523-2.[2] Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv:2004.05150, 2020.[3] Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, and Jia-Yu Pan. AirConcierge: Generating task-oriented dialogue via efficient large-scale knowledge retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 884–897, Online, November 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.79. URL https://aclanthology.org/2020.findings-emnlp.79.[4] Glen Coppersmith, Ryan Leary, Patrick Crutchley, and Alex Fine. Natural language processing of social media as screening for suicide risk. Biomedical informatics insights, 10:1178222618792860, 2018.[5] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-1423. URL https://aclanthology.org/N19-1423.[6] Shahla Farzana, Mina Valizadeh, and Natalie Parde. Modeling dialogue in conversational cognitive health screening interviews. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1167–1177, Marseille, France, May 2020. European Language Resources Association. ISBN 979-10-95546-34-4. URL https://aclanthology.org/2020.lrec-1.147.[7] Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. Knowledge-aware assessment of severity of suicide risk for early intervention. In The World Wide Web Conference, WWW ’19, page 514–525, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450366748. doi: 10.1145/3308558.3313698. URL https://doi.org/10.1145/3308558.3313698.[8] ”Jonathan Gratch, Ron Artstein, Gale Lucas, Giota Stratou, Stefan Scherer, Angela Nazarian, Rachel Wood, Jill Boberg, David Devault, Stacy Marsella, David Traum, Albert ””Skip”” Rizzo, and Louis-Philippe Morency”. The distress analysis inter- view corpus of human and computer interviews. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland, may 2014. European Language Resources Association (ELRA). ISBN 978-2-9517408-8-4.[9] Nancy Green, Curry Guinn, and Ronnie Smith. Assisting social conversation be- tween persons with Alzheimer’s disease and their conversational partners. In Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies, pages 37–46, Montréal, Canada, June 2012. Association for Computational Linguistics. URL https://aclanthology.org/W12-2906.[10] Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, and Zi Huang. Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1):214–226, 2021. doi: 10. 1109/TCSS.2020.3021467.[11] Miaofeng Liu, Yan Song, Hongbin Zou, and Tong Zhang. Reinforced training data selection for domain adaptation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1957–1968, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1189. URL https://aclanthology.org/P19-1189.[12] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 142–150, Portland, Oregon, USA, June 2011. Association for Computational Linguistics. URL http://www.aclweb.org/anthology/ P11-1015.[13] Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, and Rajiv Ratn Shah. SNAP-BATNET: Cascading author profiling and social network graphs for suicide ideation detection on social media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 147–156, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-3019. URL https://www.aclweb.org/anthology/N19-3019.[14] Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pages 271–278, Barcelona, Spain, July 2004. doi: 10.3115/1218955.1218990. URL https://aclanthology.org/P04-1035.[15] Alex Rinaldi, Jean Fox Tree, and Snigdha Chaturvedi. Predicting depression in screening interviews from latent categorization of interview prompts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7–18, Online, July 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.2. URL https://aclanthology.org/2020.acl-main.2.[16] Ramit Sawhney, Harshit Joshi, Saumya Gandhi, and Rajiv Ratn Shah. Towards ordinal suicide ideation detection on social media. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pages 22–30, 2021.[17] Annika Marie Schoene, Alexander Turner, Geeth Ranmal De Mel, and Nina Dethlefs. Hierarchical multiscale recurrent neural networks for detecting suicide notes. IEEE Transactions on Affective Computing, pages 1–1, 2021. doi: 10.1109/TAFFC.2021. 3057105.[18] Faisal Muhammad Shah, Farsheed Haque, Ragib Un Nur, Shaeekh Al Jahan, and Zarar Mamud. A hybridized feature extraction approach to suicidal ideation detection from social media post. In 2020 IEEE Region 10 Symposium (TENSYMP), pages 985–988, 2020. doi: 10.1109/TENSYMP50017.2020.9230733.[19] Tan Thongtan and Tanasanee Phienthrakul. Sentiment classification using document embeddings trained with cosine similarity. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguistics: Student Research Workshop, pages 407–414, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-2057. URL https://aclanthology.org/P19-2057.[20] Wei Wei, Quoc Le, Andrew Dai, and Jia Li. AirDialogue: An environment for goal-oriented dialogue research. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3844–3854, Brussels, Belgium, October-November 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1419. URL https://aclanthology.org/D18-1419.[21] Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le. Unsupervised data augmentation for consistency training. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS’20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546.[22] Zhongzhi Xu, Yucan Xu, Florence Cheung, Mabel Cheng, Daniel Lung, Yik Wa Law, Byron Chiang, Qingpeng Zhang, and Paul S.F. Yip. Detecting suicide risk us- ing knowledge-aware natural language processing and counseling service data. Social Science Medicine, 283:114176, 2021. ISSN 0277-9536. doi: https://doi.org/10. 1016/j.socscimed.2021.114176. URL https://www.sciencedirect.com/science/ article/pii/S0277953621005086.[23] Hannah Yao, Sina Rashidian, Xinyu Dong, Hongyi Duanmu, Richard N Rosenthal, and Fusheng Wang. Detection of suicidality among opioid users on reddit: Machine learning–based approach. J Med Internet Res, 22(11):e15293, Nov 2020. ISSN 1438-8871. doi: 10.2196/15293. URL http://www.jmir.org/2020/11/e15293/.[24] Pingyue Zhang, Mengyue Wu, Heinrich Dinkel, and Kai Yu. Depa: Self-supervised audio embedding for depression detection. In Proceedings of the 29th ACM International Conference on Multimedia, pages 135–143, 2021. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201360 en_US
