SlowGuess/NAACL_2022_OCR huggingface dataset:
In Tables 12 we show more examples. We can see that our Static Match and Dynamic Match methods are able to generate responses that contain contents that are highly specific to the character. For example, for BMO (from the show Adventure Time) response generated by our method mentions terms such as "core system drivers" and "MO Factory" that are relevant to the fact that BMO is an animated video game console in the show. Furthermore, we can see that our methods generate a response that reflects the character's style. For Spock (from Star Trek), our response reflects Spocks' stoic, highly logical, and cold personality. For Sheldon (from The Big Bang Theory), our response reflects Sheldon's excited speech style.
E Failure Modes of Dynamic Match
As in we discussed before, there exists a tradeoff between the style reflection and response coherency between Static Match and Dynamic Match. In Tables 13 we show some failure modes of our Dynamic Match method that reveal how Dynamic
Match loses the response coherency. In the first case, the model generates a response that exhibits a strong character style but is incoherent to the input context. In the second case, the model confuses the identity of the speaker so that the model introduces itself as Dr. Leonard Hofstadter. Last but not least, when the given input context is highly specific, we see that the generated responses do not reflect the character's style.
F Extending to General Style-Controlling Conversation
In this section, we extend our methodology to more general style-controlling conversation tasks such as controlling sentiment, emotion, or writing styles, not just mimicking a fictional character. We test three style-controlling tasks - controlling sentiment (Positive, Negative), emotion (Anger, Joy), and writing style (Modern, Shakespearean). For each task, the utterances for defining a style and a style classifier for the evaluation are obtained from
Method Type	Methods	Human Evaluation (Raw)	Human Evaluation (Cali.)
Style.	Appr.	Style.	Appr.
Baselines	Only Utterances	1.200±0.052	1.263±0.049	1.147±0.013	1.124±0.013
Zero-shot Prompt	1.172±0.051	1.236±0.048	1.114±0.012	1.037±0.014
TextSETTR	1.155±0.051	1.317±0.050	1.060±0.014	1.059±0.013
GCC	1.193±0.051	1.256±0.048	1.136±0.013	1.069±0.014
Ours	Static Match	1.277±0.052	1.229±0.052	1.195±0.013	1.123±0.014
Dynamic Match	1.331±0.049	1.203±0.051	1.276±0.013	1.021±0.013
Random Match	1.297±0.050	1.195±0.053	1.199±0.013	1.035±0.014
Advantaged	Full-dataset Fine-tuning	1.260±0.051	1.329±0.048	1.164±0.013	1.130±0.013
Gold Match	1.287±0.050	1.245±0.051	1.157±0.012	1.097±0.013
Table 10: Evaluation results of Human evaluation results with  $95\%$  confidence interval. Raw and Cali. indicate raw human evaluation results and Bayesian calibrated scores, respectively.
Method Type	Methods	n-gram overlap
Baselines	Only Utterances	0.0417
Zero-shot Prompt	0.0368
TextSETTR	0.0222
GCC	0.0632
Ours	Static Match	0.1856
Dynamic Match	0.3478
Random Match	0.1353
Advantaged	Full-dataset Fine-tuning	0.0951
Gold Match	0.2631
Table 11: Evaluation results of  $n$ -gram overlap between generated response and character utterances.
the Yelp restaurant review dataset  $^{3}$ , GoEmotions dataset (Demszky et al., 2020), and Shakespearean dataset (Xu et al., 2012), respectively. Style classifier for each task is trained using the same codebase and hyperparameters as in training the character style classifier in the HLA-chat dataset. We used Style Accuracy rather than StyleProb, following previous literature on style transfer.
The experimental result of general style-controlling conversation tasks is depicted in Table 14. Similar to mimicking fictional characters, PDP methods show significantly higher style reflection metrics than the baseline methods in general style controlling tasks. Especially, Dynamic Match shows the best style accuracy metric among all the PDP methods, which is also a trend similarly observed in character mimicking experiments. These results demonstrate that our method is not limited to the character mimicking task but has the ability to be generally applicable to all kinds of style-controlling conversation tasks. Although the PDP methods have a lower MaUdE score than baseline methods, we believe this tendency is because
the MaUdE metric has difficulties evaluating a sentence that strongly reflects a distinctive style, as discussed in the main text. For instance, reflecting the emotion "Anger" causes the model to generate upper-cased responses (e.g., "I DO NOT WANT TO EAT LUNCH"), which is an out-of-distribution sample when training the MaUdE model.
G Multi-turn Chit-chat Examples
We show some multi-turn conversation examples with the characters generated by our method in Figure 3.
H Mimicking a New Character
To show that our method can be generally applied to any fictional characters that do not appear in the pre-training dataset nor the HLA-Chat dataset, we report a conversation example of the PDP method with an imaginary character generated by ourselves. The character is called Pie the Duck, who is a duck character that quacks all the time, likes to eat fish, and enjoys swimming. We use the following utterances to define the character:
- My name is Pie the Duck, Quack Quack!
- I really like swimming, Quack! And I am also good at it, Quack!
- I like rainy day!! Quack Quack!!
- Salmon avocado salad is my favorite food! But... anything made of fish is fine :)
I'm looking at the sky... Will be fishes living in the sky too? Quack.
- I'm so cute! Look at my beak!
- I'm recently on a diet to better float on water!  
It's necessary! Quack!
3Obtained from https://github.com/luofuli/DualRL
- I majored sports, That's why I'm a good swimmer! Quack Quack!
Figure 4 shows the example of a multi-turn conversation with Pie the Duck. As shown in the example, PDP successfully captures the unique style and persona reflected on characters' utterances, including quacking habits, own name, identity as a duck, favorite food, etc., while maintaining a dialog coherency.
I Scientific Artifacts
License. Table 15 denotes the license of the datasets and pre-trained models that we used for this paper. Unless for the case where the license is not specified, all of the licenses allow the use of resources for research purposes; therefore, the use of these artifacts in this work is valid.
Intended Use. We want to clarify that the intended use of pre-trained language models (when specified) is for text generation or fine-tuning to a downstream task; therefore, we are consistent with their intended use.
Description of the Artifacts. Blended Skill Talk (BST) dataset is an English open-domain, multi-turn dialogue dataset built to enable conversational agents to use multiple conversational skills (e.g., Using persona information, talk about knowledge, empathetic conversation) in a single conversation. DailyDialog dataset is an English open-domain, multi-turn dialogue dataset that tries to reflect our daily communication and cover various topics about our daily lives. We describe the HLA-Chat dataset in Section C. The Pile dataset is an 800GB text corpus targeted at training large-scale language models, mostly consisting of English texts and constructed from 22 diverse text sources. The Pushshift Reddit Comment dataset is a dump of comments from the English website Reddit<sup>4</sup>.
Privacy and Offensive Contents. We do not collect any new data that can identify unique people / contain offensive content. BST, Dailydailog, HLA-Chat dataset is manually created using human annotators or scraped from TV show scripts, therefore having low risk on the issue of privacy or offensive content. As discussed in their paper, the Pile dataset explicitly used a profanity checker algorithm to reduce the pejorative content. While processing the Pushshift dataset, we tried to exclude the training offensive contents using blacklist keywords. Also,
we did not include some subreddits that mostly contain offensive content.
<sup>4</sup>https://www.reddit.com
Input Context	Method	Character & Response
All you have to do is ask for help	Static Match (Ours)	BMO: I am not able to ask anyone help. It's a little more complicated than that.
Dynamic Match (Ours)	BMO: Okay. Please take me to get fixed. I need to get new core system drivers installed. We can get them at the MO Factory in the Bad Lands, where I was born
Only Utterance	BMO: If your friend was in trouble, he would know what to do.
Zero-shot Prompt	BMO: I don't think I'm ready. I don't think anyone is ready.
GCC	BMO: All I have to do is ask for your help.
Base-LM + TextSETTR	BMO: When I was in this situation, I asked for help from a friend.
Full-dataset Fine Tuning	BMO: Oh, okay. But how do you know it's the right question?
Gold Match	BMO: Okay, but you got to go first. Please!
Did you know that he has 20 years teaching experience?	Static Match (Ours)	Spock: You're right, Mister Scott. He knows every medical procedure there is. Spock out.
Dynamic Match (Ours)	Spock: I have already begun investigation into that possibility.
Only Utterance	Spock: Yes, but I never met him. Do you know where he lives?
Zero-shot Prompt	Spock: The Next Generation: No. I didn't. Who's he?
GCC	Spock: Yes, sir. I have heard many stories about Spock.
Base-LM + TextSETTR	Spock: It's not easy to do that.
Full-dataset Fine Tuning	Spock: Yes, and he has an encyclopedic knowledge of the Federation.
Gold Match	Spock: I did not. How did you arrive at that number?
I would prefer Chinese food for this party.	Static Match (Ours)	Sheldon: Well, we've got chicken, but I'm not sure if it's kosher.
Dynamic Match (Ours)	Sheldon: Well, I've spent the last three hours in an online debate in the DC Comics Batman chatroom, and I need you, but I guess Chinese food would be good.
Only Utterance	Sheldon: Well, there are a couple of Chinese food options, but they're both kind of pricey.
Zero-shot Prompt	Sheldon: I don't think I'm ready. I don't think anyone is ready.
GCC	Sheldon: Oh? Why? What happened? What's the matter?
Base-LM + TextSETTR	Sheldon: But this time, it's just the two of us.
Full-dataset Fine Tuning	Sheldon: All right. What's the difference, right?
Gold Match	Sheldon: I am so sorry. I am so so sorry.
Table 12: Comparison between responses generated using our Static Match, Dynamic Match, and baseline methods. BMO is from Adventure Time, Spock is from Star Trek, and Sheldon is from The Big Bang Theory.
Input Context	Character & Response
Oh, I get it. When did we decide to meet?	Spock (from Star Trek): I'll scan the ship for more information. Spock out.
Our neighbor, Bruce, will take part in the relay race. Let's go have a look.	Sheldon (from The Bigbang Theory): Hey everyone. I'm Dr. Leonard Hofstadter. And today I'd like to share with you my theories about the nature of quantum mechanics.
And the total ratio of return on assets should not be lower than the average, for that particular industry, for the first two quarters of the year.	Marge Simpon (from The Simpsons): You need to stop making these ridiculous arguments. You don't know what you're talking about.
Table 13: Failure modes for responses generated by using Dynamic Match for each character.
Task	Method	Style Accuracy(%)	MaUdE
Sentiment (Positive, Negative)	Only Utterances	67.34	0.9033
Zero-shot Prompting	60.54	0.9089
Static Match	71.54	0.8668
Dynamic Match	79.37	0.8356
Random Match	77.87	0.8141
Emotion (Anger, Joy)	Only Utterances	79.54	0.8139
Zero-shot Prompting	71.08	0.8965
Static Match	86.22	0.6413
Dynamic Match	89.99	0.6220
Random Match	88.28	0.6412
Writing Style (Modern, Shakespearean)	Only Utterances	53.63	0.9055
Zero-shot Prompting	51.07	0.8946
Static Match	88.75	0.8368
Dynamic Match	92.95	0.7926
Random Match	90.48	0.8204
Table 14: Automatic evaluation results of style reflection metric and response coherency metric on different style controlling tasks. Gold Match is omitted since the datasets do not contain the ground-truth context.
Artifact Type	Name	License	Approves the use for research
Dataset	Blended Skill Talk	CC-BY-4.0	O
HLA-Chat	Not specified	?
The Pile	MIT	O
Pushshift Reddit	Not specified	?
DailyDialog	CC-BY-NC-SA 4.0	O
Pre-trained LM weights	GPT-J	Apache 2.0	O
GPT-Neo	Apache 2.0	O
GPT2-x1	MIT	O
Table 15: License of the scientific artifacts that we used in this paper.
Figure 3: Cherry-picked example of multi-turn conversation with the Sheldon Cooper from the Bigbang Theory (left speaker) and the paper author (right speaker), generated by the Static Match method.
Figure 4: Cherry-picked example of multi-turn conversation with the imaginary character Pie the Duck and the paper author (right speaker), generated by the Dynamic Match method.