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題名 自然語言推理之後設可解釋性建模
Modeling Meta-Explainability of Natural Language Inference
作者 蔡鎮宇
Tsai, Chen-Yu
貢獻者 黃瀚萱<br>陳宜秀
Huang, Hen-Hsen<br>Chen, Yi-Hsiu
蔡鎮宇
Tsai, Chen-Yu
關鍵詞 自然語言處理
自然語言理解
自然語言推理
可解釋性
可解釋人工智慧
注意力機制
信任度評估
nlp
nlu
nli
explainability
interpretability
trust evaluation
attention mechanism
explainable AI
日期 2020
上傳時間 2-Sep-2020 13:08:22 (UTC+8)
摘要 本研究之主軸為利用注意力機制，在自然語言推理任務上，以自然語言形式之解釋賦予模型可解釋性，並進行人類信任度評估。近年來人工智慧系統的可解釋性逐漸受到重視，可解釋性使開發者、相關開發人員及終端使用者能夠了解人工智慧系統，進而得以設計更完備的系統、產品以及更符合使用者需求的應用產品。注意力機制做為系統原生的可解釋機制，能夠同時提供忠實且合理的解釋，目前於注意力機制可解釋性之研究，多以注意力權重進行視覺化的呈現來賦予模型決策可解釋性，然而在一般互動的情境中，解釋多是以自然語言的方式表達。而在可解釋性的評估部分，目前所採用的評估方式甚少加入終端使用者—人類進行評估；若有，其評估方式之完備性也難以為人工智慧系統之應用部署提供洞見。

本研究利用 Transformer 架構模型之注意力機制，以自然語言之方式呈現其解釋，賦予模型可解釋性；同時探討在提供不同任務知識後，對於此方法之解釋有何影響；最後以不同模型之解釋進行人類信任度之評估，分析人類對於解釋之信任及偏好。實驗顯示，在自然語言推理任務上，模型之效能與注意力關注區間確實相關；在加入不同特性之任務知識後，模型的解釋能夠忠實地呈現其訓練任務之特性；最後在人類信任度上，人類對於解釋方式偏好不盡相同，但是長而資訊豐富的解釋方式，較短而精確的解釋方式來得有優勢。
The explainability of artificial intelligence (AI) model has recently attracted much interest from the researchers. Explainability provides developers, stakeholders and end users with a better understanding of how the model works and can assist in better interaction between human and machine. Attention mechanism, as an intrinsic explainable method, is considered more suitable for faithful and plausible explanations. The majority of research on attention mechanism, however, focuses on visualization of the attention weight as a way to make the model explainable. Yet in real-life interactions, explanations are more likely presented in natural language. Furthermore, while evaluating model explainability, little research has taken human responses into consideration or included measurement of human reactions. The void of human-related research led to absence of useful insights to develop and deploy AI applications.

This research employs natural language inference paradigm, using transformer-based attention weight to provide explanations of the task performance of the model. After the training, we also evaluate human trust and preference towards the explanation provided by different models. The results indicate that in natural language inference tasks, the model performance, and , long, contextual explanations are more advantageous than short, concise explanation in gaining human trust.
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L. Arras, F. Horn, G. Montavon, K.-R. M¨uller, and W. Samek. Explaining predictions of non-linear classifiersinnlp. InProceedingsofthe1stWorkshoponRepresentation Learning for NLP, pages 1–7. ACL, 2016
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Liu, Yinhan & Ott, Myle & Goyal, Naman & Du, Jingfei & Joshi, Mandar & Chen, Danqi & Levy, Omer & Lewis, Mike & Zettlemoyer, Luke & Stoyanov, Veselin. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach.
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T. Miller, “Explanation in Artificial Intelligence: Insights from the Social Sciences”, arXiv preprint arXiv: 1706.07269, 2017.
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Montavon, Grégoire & Samek, Wojciech & Müller, Klaus-Robert. (2018). Methods for Interpreting and Understanding Deep Neural Networks. Digital Signal Processing. 73. 1-15. 10.1016/j.dsp.2017.10.011.
Mueller, Shane & Hoffman, Robert & Clancey, William & Emrey, Abigail & Klein, Gary. (2019). Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI.
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描述 碩士
國立政治大學
數位內容碩士學位學程
107462009
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107462009
資料類型 thesis
dc.contributor.advisor 黃瀚萱<br>陳宜秀zh_TW
dc.contributor.advisor Huang, Hen-Hsen<br>Chen, Yi-Hsiuen_US
dc.contributor.author (Authors) 蔡鎮宇zh_TW
dc.contributor.author (Authors) Tsai, Chen-Yuen_US
dc.creator (作者) 蔡鎮宇zh_TW
dc.creator (作者) Tsai, Chen-Yuen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Sep-2020 13:08:22 (UTC+8)-
dc.date.available 2-Sep-2020 13:08:22 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2020 13:08:22 (UTC+8)-
dc.identifier (Other Identifiers) G0107462009en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131902-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 數位內容碩士學位學程zh_TW
dc.description (描述) 107462009zh_TW
dc.description.abstract (摘要) 本研究之主軸為利用注意力機制，在自然語言推理任務上，以自然語言形式之解釋賦予模型可解釋性，並進行人類信任度評估。近年來人工智慧系統的可解釋性逐漸受到重視，可解釋性使開發者、相關開發人員及終端使用者能夠了解人工智慧系統，進而得以設計更完備的系統、產品以及更符合使用者需求的應用產品。注意力機制做為系統原生的可解釋機制，能夠同時提供忠實且合理的解釋，目前於注意力機制可解釋性之研究，多以注意力權重進行視覺化的呈現來賦予模型決策可解釋性，然而在一般互動的情境中，解釋多是以自然語言的方式表達。而在可解釋性的評估部分，目前所採用的評估方式甚少加入終端使用者—人類進行評估；若有，其評估方式之完備性也難以為人工智慧系統之應用部署提供洞見。

本研究利用 Transformer 架構模型之注意力機制，以自然語言之方式呈現其解釋，賦予模型可解釋性；同時探討在提供不同任務知識後，對於此方法之解釋有何影響；最後以不同模型之解釋進行人類信任度之評估，分析人類對於解釋之信任及偏好。實驗顯示，在自然語言推理任務上，模型之效能與注意力關注區間確實相關；在加入不同特性之任務知識後，模型的解釋能夠忠實地呈現其訓練任務之特性；最後在人類信任度上，人類對於解釋方式偏好不盡相同，但是長而資訊豐富的解釋方式，較短而精確的解釋方式來得有優勢。		zh_TW
dc.description.abstract (摘要) The explainability of artificial intelligence (AI) model has recently attracted much interest from the researchers. Explainability provides developers, stakeholders and end users with a better understanding of how the model works and can assist in better interaction between human and machine. Attention mechanism, as an intrinsic explainable method, is considered more suitable for faithful and plausible explanations. The majority of research on attention mechanism, however, focuses on visualization of the attention weight as a way to make the model explainable. Yet in real-life interactions, explanations are more likely presented in natural language. Furthermore, while evaluating model explainability, little research has taken human responses into consideration or included measurement of human reactions. The void of human-related research led to absence of useful insights to develop and deploy AI applications.

This research employs natural language inference paradigm, using transformer-based attention weight to provide explanations of the task performance of the model. After the training, we also evaluate human trust and preference towards the explanation provided by different models. The results indicate that in natural language inference tasks, the model performance, and , long, contextual explanations are more advantageous than short, concise explanation in gaining human trust.		en_US
dc.description.tableofcontents 摘要 4
Abstract 5
目錄 6
圖目錄 9
表目錄 10
第一章 12
1.1 問題背景與研究動機 12
1.2 問題定義與研究策略 14
1.3 相關研究 16
1.4 主要貢獻 18
第二章 19
2.1 可解釋之人工智慧（Explainable Artificial Intelligence, XAI） 19
2.1.1 可解釋性之重要性 19
2.1.2 解釋模型的方法 21
2.1.3 評估模型可解釋性 24
2.1.4 人類感知 25
2.2 自然語言理解（Natural Language Understanding） 27
2.2.1 自然語言理解概覽 27
2.2.2 自然語言推理（Natural Language Inference） 29
2.2.3 自然語言推理模型 30
2.3 注意力機制（Attention Mechanism） 33
2.3.1 注意力機制原理及應用 33
2.3.2 Transformer 模型 34
2.3.3 基於 Transformer 架構之模型 36
2.3.4 Transformer 模型之自注意力機制可解釋性 39
第三章 42
3.1自然語言推理（Natural Language Inference） 42
3.1.1 文字蘊含關係識別 42
3.1.2 建立可解釋之模型 43
3.2自注意力機制 45
3.3 XLNet 48
3.3.1 自迴歸與自編碼模型 48
3.3.2 Permutation Language Modeling 50
3.3.3 雙流自注意力 51
3.4 多任務學習（Multi-task Learning） 54
3.5 後設可解釋性研究 55
3.5.1 標註注意力重要區間 55
3.5.2 模型可解釋性評估 56
3.5.3 可解釋性之後設建模 56
3.5.4 人類認知與模型可解釋性 57
第四章 65
4.1 實驗設定 65
4.1.1 資料集 65
4.1.2 效能評估指標 66
4.1.3 可解釋性評估指標 67
4.2 可解釋性評估 68
4.2.1 注意力分數與可解釋性 68
4.2.2 關鍵字可解釋性評估 70
4.2.3 蘊含關係辨識與可解釋性 71
4.3 後設可解釋性建模 77
4.3.1 後設可解釋性評估 77
4.3.2 後設可解釋性與蘊含關係 79
4.3.3 後設可解釋性案例分析 80
4.3 在 SNLI 資料集之適應性 84
4.4 人類認知與模型可解釋性 85
4.4.1 人類解釋機制與模型之異同 85
4.4.2 人類信任度及模型解釋偏好評估 93
第五章 109
參考文獻 112
附錄 121
1.1 信任度評估實驗內容 121		zh_TW
dc.format.extent 4037234 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107462009en_US
dc.subject (關鍵詞) 自然語言處理zh_TW
dc.subject (關鍵詞) 自然語言理解zh_TW
dc.subject (關鍵詞) 自然語言推理zh_TW
dc.subject (關鍵詞) 可解釋性zh_TW
dc.subject (關鍵詞) 可解釋人工智慧zh_TW
dc.subject (關鍵詞) 注意力機制zh_TW
dc.subject (關鍵詞) 信任度評估zh_TW
dc.subject (關鍵詞) nlpen_US
dc.subject (關鍵詞) nluen_US
dc.subject (關鍵詞) nlien_US
dc.subject (關鍵詞) explainabilityen_US
dc.subject (關鍵詞) interpretabilityen_US
dc.subject (關鍵詞) trust evaluationen_US
dc.subject (關鍵詞) attention mechanismen_US
dc.subject (關鍵詞) explainable AIen_US
dc.title (題名) 自然語言推理之後設可解釋性建模zh_TW
dc.title (題名) Modeling Meta-Explainability of Natural Language Inferenceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Adadi, Amina & Berrada, Mohammed. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access. PP. 1-1. 10.1109/ACCESS.2018.2870052.
Review of "Natural language understanding" by James Allen. Benjamin/Cummings 1987.
L. Arras, F. Horn, G. Montavon, K.-R. M¨uller, and W. Samek. Explaining predictions of non-linear classifiersinnlp. InProceedingsofthe1stWorkshoponRepresentation Learning for NLP, pages 1–7. ACL, 2016
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dc.identifier.doi (DOI) 10.6814/NCCU202001576en_US