論文名稱：Counterfactual Off-Policy Training for Neural Dialogue Generation 論文作者：朱慶福，張偉男，劉挺，王威廉 原創作者：朱慶福 論文連結：https://arxiv.org/abs/2004.14507 轉載須標註出處：哈工大SCIR
2. 模型結構
2.1 結構因果模型（Structural Causal Model）
2.2 干預（Intervention）
2.3 反事實推理（Counterfactual Inference）
3. 實驗結果
4. 實驗分析
5. 結論
參考文獻
[1] Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic Books.
[2] Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sebastien Racaniere, Arthur Guez, and Jean Baptiste Lespiau. 2019. Woulda, coulda, shoulda: Counterfactually-guided policy search. In Proceedings of the Seventh International Conference on Learning Representations.
[3] Michael Oberst and David Sontag. 2019. Counterfactual off-policy evaluation with gumbel-max structural causal models. In International Conference on Machine Learning, pages 4881–4890.
[4] Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.
[5] Jingjing Xu, Xuancheng Ren, Junyang Lin, and Xu Sun. 2018. Diversity-promoting GAN: A cross-entropy based generative adversarial network for diversified text generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3940–3949.
[6] Jiwei Li, Will Monroe, Tianlin Shi, Se ́bastien Jean, Alan Ritter, and Dan Jurafsky. 2017a. Adversarial learning for neural dialogue generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2157–2169.
[7] Yi-Lin Tuan and Hung-Yi Lee. 2019. Improving conditional sequence generative adversarial networks by stepwise evaluation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(4):788–798.