SlowGuess/NAACL_2022_OCR huggingface dataset:
Yan Xu, Etsuko Ishii, Zihan Liu, Genta Indra Winata, Dan Su, Andrea Madotto, and Pascale Fung. 2021. Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters. CoRR, abs/2105.06232.
Mi Zhang, Jie Tang, Xuchen Zhang, and Xiangyang Xue. 2014. Addressing cold start in recommender systems: A semi-supervised co-training algorithm. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '14, page 73-82, New York, NY, USA. Association for Computing Machinery.
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing dialogue agents: I have a dog, do you have pets too? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2204-2213, Australia. Association for Computational Linguistics.
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2020. DIALOGPT: Large-scale generative pre-training for conversational response generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 270-278, Online. Association for Computational Linguistics.
Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, and Rui Yan. 2019. A document-grounded matching network for response selection in retrieval-based chatbots. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pages 5443-5449. International Joint Conferences on Artificial Intelligence Organization.
Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, and Xuan Zhu. 2019. Personalized Dialogue Generation with Diversified Traits. CoRR, abs/1901.09672.
Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao. 2020. Towards persona-based empathetic conversational models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6556-6566, Online. Association for Computational Linguistics.
Wangchunshu Zhou, Qifei Li, and Chenle Li. 2021. Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description. CoRR, abs/2111.15093.
Yicheng Zou, Zhihua Liu, Xingwu Hu, and Qi Zhang. 2021. Thinking clearly, talking fast: Concept-guided non-autoregressive generation for open-domain dialogue systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2215-2226, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Figure 4: Change of the testing PPL during RL.
The PERSONACHAT dataset used in this work is well-known and widely used. In our view, there is no known ethical issue with its usage. Large-scale pre-trained models are also employed, but they are widely known to be subject to potential problems such as generating offensiveness context. With its use, our partner personas generator could generate unseen personas, which are also subject to potential offensive generation. An offensiveness check can be incorporated to alleviate this problem for actual usage (Baheti et al., 2021).
For supervised phase, we set Adam (Kingma and Ba, 2015) as our optimizer, with hyperparameters  $\eta = 5e - 4$ ,  $\beta_{1} = 0.9$ ,  $\beta_{2} = 0.999$ ,  $\epsilon = 1e - 8$ . The models are fine-tuned for 2 epochs. For RL phase, we set Adam as our optimizer, with  $\eta = 5e - 6$ ,  $\beta_{1} = 0.9$ ,  $\beta_{2} = 0.999$ ,  $\epsilon = 1e - 8$ . We update the model parameters every 20 training instances and validate the model performance every 50 updates. DistilBERT (Sanh et al., 2019) is used to initialize the model parameters for the critic network. We set Adam as our optimizer, with hyperparameters  $\eta = 5e - 6$ ,  $\beta_{1} = 0.9$ ,  $\beta_{2} = 0.999$ ,  $\epsilon = 1e - 8$ . We fine-tune the critic for 1 epoch, and we freeze it empirically during RL. All the experiments are conducted based on the TRANSFORMERS library from HuggingFACE (Wolf et al., 2020).
C Analysis on Dialogue Reponse Generation
We present the progressive change of the testing perplexity for DRG and PPG on PERSONACHAT-ORI in Figure 4. We observe that they improve
simultaneously, which supports our motivation to use RL for joint training.
D Human Evaluation Criteria
- (Appropriateness): "Who is more appropriate given the previous dialogue context?"
- (Informativeness): "Who is more diverse instead of null answers such as I do not know?"
(Engagingness): "Who would you prefer to talk with for a long conversation?"
- (Human-likeness): "Which speaker do you think sounds more like a real person?"
- (Coherence): "Which persona contains traits that are more coherent to each other?"
- (Interestingness): "Which persona is more interesting and diverse?"
The first four are from the existing work (Li et al., 2019; Zou et al., 2021) and we propose the last two for evaluating PPG. We report the first four for DRG, and we report the last four for PPG.
E Dataset Limitations
Our work uses an off-the-shelf persona-based conversational dataset PERSONACHAT (Zhang et al., 2018), which is collected and built by crowdsourcing to converse based on a fake set of discrete traits. There is no personal information and hence no ethics concern, but this might result in limited usefulness as there could be discrepancies between the collected samples and real-life conversation. It is also more expensive to collect real data. However, PERSONACHAT has been widely used by the community as a standard dataset. Many well-known persona-based datasets suffer from the same problem (Urbanek et al., 2019) as widely known.
Although Mazaré et al. (2018) proposed a useful method to collect large-scale persona-based dialogue datasets by extracting persona from user comments with classifiers trained on revised personas from PERSONCHAT which can improve the model performance on PERSONCHAT. For legal reasons, they did not release this dataset at the time of writing. Similarly, Zheng et al. (2019) proposed a persona-based dialogue dataset with diversified traits, but it is not currently online readily available.
Zhong et al. (2020) has followed the approach suggested by Mazaré et al. (2018) to build an empathetic conversation dataset based on personas.
4The result is scaled for the sake of space and clarity.
Generated Partner Personas
Personas A: I am a shy person, but I love to sing. Until recently, I ve never been able to sing in front of anyone. Anyways, I decided to give it a try and participated in an audition for a talent show. My shyness made me panick and I didn't show up.
Personas B: I play the violin. I am married with 5 kids. I am nurse. I met my husband when I was a freshman in college.
Personas C: I am a soccer player. I am a goalie. My number is 42. Nike cleats are my favorite. I joined a new team last month.
Personas D: I have two kids, ages 2 and 6. I am from sterling heights, michigan. My favorite movie is titanic. I work part time at aldis. My husband owns a small auto repair shop.
Personas E: I am a retired computer programmer. I have one grandson and one daughter. I just turned 77. I love animals. I like watching british tv shows and movies.
Personas F: I like to go hunting. I like to remodel homes. I like to shoot a bow. My favorite holiday is halloween. I like to go shopping with my daughters.
Personas G: I have a large cd collection. I collect stamps. Favorite band is the beetles. I like vintage furniture.
Personas H: I like to drink wine. I enjoy reading history books. I am a teacher. I love to write stories while sitting in the grass in my back yard. I grew up in new hampshire.
Personas I: I am retired. I stay active. I have eight grandchildren. I have good health.
Personas J: I m a student. I like to go out to eat. I like listening to other rap music too. One of my favorite artists is drake. A hobby of mine is the drums. I also enjoy cooking.
Personas K: I have two children. I like to go on walks. I am from mexico. I used to be a chef, but I am a teacher now. I like to bake.
Personas L: I like to do all my shopping at Walmart. I m deathly terrified of heights. I prefer to live where the weather s cold. Winter s my favorite time of the year. I m really excited to see how game of thrones ends.
Personas M: I like to cook. I am a foodie. I love to chat with my friends. I love kids and dogs. I like to go shopping with my daughters.
Personas N: My family hates my fiancee. We will be traveling to niagra falls for our honeymoon. We are getting married in a park. My dog is the ring bearer.
Personas O: My favorite color is red. I have 2 dogs as pets. I leave the dogs home when I visit my parents. I love dogs. I work as a veterinarian s assistant.
Personas P: I am a musician. I wish I could spend more time at home. I like to write my own songs. I have taken formal music lessons since i was 5.
Personas Q: I love comics. I love reading. I ve started creating my own comics and presenting them to publishers. I decided to publish my creations on internet. I ve been rejected several times and thought of giving up with this.
Table 8: More generated personas. We highlight in pink for informativeness and in yellow for coherence.
However, their main focus is to investigate the impact of personas on empathetic dialogue generation. Therefore, we choose to follow the community to investigate our method on the most well-known dataset, PERSONCHAT.
F Computing Infrastructure
We run all our experiments on a single NVIDIA TI-TAN RTX with 24GB GPU memory. Fine-tuning
the generators for 2 epochs as we have done on our preprocessed PERSONCHAT train split consumes about 3-4 hours. Fine-tuning our critic classifier for 1 epoch consumes about 1 hour. Our RL phase consumes about 15 hours to achieve the best validation loss before being early stopped. We report averaged results from 3 runs for our dialogue response generation and partner personas generation results reported in Table 1, Table 4 and Table 7.