博碩士論文 106423005 詳細資訊




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姓名 黃俊杰(Jun-Jie Huang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 基於注意力機制的開放式對話系統
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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 (2022-7-31以後開放)
摘要(中) 近年許多研究提出各種基於神經網路的對話系統,但模擬對話仍然是對話生成領域中最棘手的挑戰之一。而大多對話系統與其相關研究仍採用基於RNN架構Seq2Seq模型,此外Transformer在Neural Machine Translation (NMT) 領域上的表現遠超於基於RNN架構的Seq2Seq模型,但鮮少研究將基於RNN的Seq2Seq模型和Transformer模型在對話生成領域上進行評估和比較,且對話生成模型的評估方式仍然無法使用單一的評估基準來對模型的生成回應進行評估。
因此本研究會採用基於RNN的Seq2Seq模型和Transformer模型,並使用二種電影字幕及對話相關資料集Cornell Movie-Dialog Corpus和OpenSubtitles Corpus進行對模型進行訓練。因資料集的特性,本篇研究也將著重在於open-domain對話模型之上進行探討,並且使用多種量化分析指標和質性分析來證實二者模型架構對於open-domain對話生成領域中的合適性,並且探討各個對話評估方式的相依性和可靠性。
從量化分析和質性分析結果顯示,基於RNN的Seq2Seq模型合適回答較短且保守的回應,Transformer模型在回應的整體質量和預測能力比基於RNN的Seq2Seq模型較高,且擅長回答推論簡單的問題,以及比後者較能夠生成較長的回應。並在本研究中找出各項評估指標的相依關係。而期望在未來研究中,將Transformer模型導入和取代基於RNN的Seq2Seq模型在不同的模型的架構和任務當中,並且將本研究評估的流程導入未來的研究當中。
摘要(英) In recent years, many studies have proposed many kinds of neural network-based dialogue systems, but analog dialogue is still one of the most difficult challenges in the field of dialogue generation. Most of the dialogue systems and related research still use the Seq2Seq model based on RNN architecture. In addition, Transformer performs much better in the field of Neural Machine Translation (NMT) than RNN-based Seq2Seq models, but few studies have evaluated and compared RNN-based Seq2Seq models and Transformer models in the field of dialog generation, and the way in which the dialog generation model is evaluated is still not able to use a single evaluation benchmark to evaluate the model′s generated response.
Therefore, this study will use RNN-based Seq2Seq model and Transformer model, and models were trained using two movie subtitles and conversation-related data sets, Cornell Movie-Dialog Corpus and OpenSubtitles Corpus. Due to the nature of the dataset, this study will also focus on the open-domain dialogue model and use a variety of quantitative analysis indicators and qualitative analysis to verify the suitability of the two model architectures in the open-domain dialog generation domain. And explore the interdependence and reliability of the various methods of dialogue evaluation.
From the results of quantitative analysis and qualitative analysis, the RNN-based Seq2Seq model is suitable for short answers and conservative responses. The overall quality and predictive power of the Transformer model is higher than that of the RNN-based Seq2Seq model, and it is good at answering simple inference questions and generating longer responses than the latter. In this study, we find the dependence of various evaluation indicators. It is expected that in the future research, the Transformer model will be introduced and replaced with the RNN-based Seq2Seq model in the architecture and tasks of different models, and the process of this research evaluation will be introduced into future research.
關鍵字(中) ★ 對話生成
★ Seq2Seq
★ 基於注意力機制模型
★ Transformer
關鍵字(英) ★ Dialogue generation
★ Seq2Seq
★ Attention based Models
★ Transformer
論文目次 中文摘要 I
Abstract II
目錄 IV
圖目錄 VII
表目錄 VIII
1. 緒論 1
1.1. 研究背景 1
1.2. 研究動機 1
1.3. 研究目的 2
1.4. 論文架構 2
2. 對話系統模型與相關研究 4
2.1. Encoder-Decoder模型 4
2.1.1. 遞歸神經網路(Recurrent Neural Networks) 4
2.1.2. Sequence-to-sequence(Seq2seq)模型 5
2.1.3. Seq2Seq模型與相關研究 7
2.2. Attention based Models 11
2.2.1. Attention機制 11
2.2.2. Transformer模型 14
2.2.3. Transformer模型之相關研究 17
2.3. 評估方法 20
2.3.1. Word overlap-based metrics – BLEU 20
2.3.2. Perplexity 21
2.3.3. Embedding-based metrics 21
2.3.4. 基於神經網路的自動評估 22
2.3.5. Human Evaluation 25
2.3.6. 對話生成之評估方法綜合比較 25
2.4. 綜合討論 29
3. 實驗方法 30
3.1. Datasets 30
3.1.1. Cornell Movie-Dialog Corpus 30
3.1.2. OpenSubtitles Corpus 31
3.2. 方法及流程 32
3.2.1. Preprocessing 32
3.2.2. 基於RNN的Seq2Seq模型建模 33
3.2.3. Transformer模型建模 33
3.2.4. 模型評估之指標 33
3.3. 實驗:模型超參數設置對於生成對話品質之影響 34
3.3.1. 基於RNN的Seq2Seq模型設置 34
3.3.2. Transformer 模型設置 35
4. 結果與分析 37
4.1. 量化分析 37
4.1.1. BLEU 37
4.1.2. Perplexity 42
4.1.3. Embedding-based metrics 42
4.1.4. RUBER 43
4.1.5. 人工問卷評估 43
4.1.6. 量化之綜合分析 44
4.2. 質性分析 47
4.2.1. 對話或問題的類別對模型生成回應的品質影響 47
4.2.2. 質性之綜合分析 49
4.3. 綜合分析 49
4.3.1. 單雙向LSTM在Seq2Seq模型 50
4.3.2. 基於RNN的Seq2Seq模型和Transformer模型 50
4.3.3. 各個評估方式之間的相依性和可靠性 51
5. 總結 52
5.1. 結論 52
5.2. 實驗貢獻 52
5.3. 未來展望 53
6. Reference 54
7. 附錄 58
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指導教授 柯士文 審核日期 2019-7-23
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