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We train our model on one Quadro RTX 6000 GPU. It costs about 25 hours to train our model, and 4 hours to generate stories using our model.
Models	B-1	B-2	BS-t	BS-m	PC
CONPER	19.1	6.9	32.1	41.4	59.7
CONPER (Rand)	17.4	6.2	26.0	38.9	52.1
CONPER (Multi)	17.9	6.6	32.6	40.0	55.1
CONPER (Sparse)	18.0	6.6	31.6	40.2	57.0
Growth Truth	N/A	N/A	42.6	42.6	75.2
Table 9: Automatic evaluation results for several variants of CONPER. The best performance is highlighted in bold. All results are multiplied by 100.
B Analysis of Extraction Strategy
B.1 Target Extraction
We regard one sentence which has the maximum BERTScore with the persona description as the target in our model. We conducted two experiments to further investigate the influence of target extraction strategy: (1) CONPER (Rand): It regards a sentence randomly sampled from the story as the target for training in the target planning stage. (2) CONPER (Multi): It regards two sentences which have the maximum BERTScore with the persona description as the target.
As shown in Table 9, when using a random sentence as the target, all the metrics drop significantly. And Table 5 in the main paper shows that it is hard for the random policy to select persona-related sentences. The results indicate the benefit of our methods for modeling relations between personas and events. Moreover, using multiple sentences as the target is inferior to using only one in terms of most metrics. It is possibly because stories in STORIUM tend to embody personas sparsely, and modeling the relations between personas and multiple persona-unrelated events directly may hurt the performance. The BS-t score is higher when using multiple sentences because more words can easily lead to a higher recall score.
B.2 Keyword Extraction
We extracted at most 5 keywords from each sentence for the plot planning stage. We also experimented with a more sparse plan by extracting only one keyword from each sentence (called CON-PER (Sparse)). Table 9 shows that using a more sparse plan performs worse in all metrics. It is possibly because the limited planning keywords could not make the best of the external knowledge to form coherent and persona-related plots.
C Analysis of Target Guidance
We visualize how target guidance affects word prediction in the plot planning stage in Figure 2. The original word distribution is weighted to those words irrelevant to the target sentence, while the bias term (Equation 18) is weighted to those words related to the target sentence in semantics such as bar. After combining the original word distribution with the bias term, the final distribution can balance the trade-off between target guidance and language model prediction. This validates our hypothesis that target guidance can draw the planned plots closer to the target, which helps improve the story coherence and persona-consistency.
Figure 2: A case showing the effect of target guidance. The planning keywords are brought closer to the target in semantics under the target guidance.
D Diversity
We compare the diversity of CONPER with baselines using distinct-  $n$  (D-  $n$  ) (Li et al., 2016), the ratio of distinct  $n$ -grams to all  $n$ -grams in generated stories. The results in Table 10 show that CONPER has better coherence and persona consistency without sacrificing the diversity.
Models	D-1	D-2	D-3	D-4
GPT2Scr	0.021	0.134	0.381	0.653
GPT2Ft	0.022	0.184	0.501	0.777
PlanAhead	0.032	0.256	0.618	0.863
CONPER	0.016	0.148	0.439	0.730
Growth Truth	0.062	0.368	0.739	0.927
Table 10: Automatic evaluation results. CONPER is comparable with fine-tuned GPT2 in diversity performance.
Models	Number of Parameters
ConvS2S	135M
Fusion	255M
GPT2	124M
PlanAhead	201M
CONPER	247M
Table 11: Number of Parameters of different models.
E Manual Evaluation
We conduct manual evaluation on Amazon Mechanical Turk. To improve the annotation quality, we provide a detailed instruction for annotators, which contains: (1) a summary of our task; (2) a formal definition for coherence and persona consistency; and (3) good and bad examples for coherence and persona consistency. The detailed evaluation guideline is shown in Figure 3.
F Model Parameters
We compute the number of parameters for some models used in our experiments. The result is shown in Table 11.
G Error Analysis
Although the proposed model outperforms the strong baselines, Table 7 in the main paper shows that there are still many generated stories that exhibit opposite or irrelevant with the given persona. Therefore, we presented some typical error cases generated by our model for each error type in Figure 4. These cases show our model still does not completely control personas in story generation. When there is a slight conflict between the generated target sentence and the given persona (e.g., you're here for fun is slightly conflict with slow to action), the generated plan would further deviate from the input under the guidance of the target sentence (e.g., excit, like), and finally the generated story exhibits an opposite persona. Similarly, when the generated target sentence is irrelevant with the given persona (e.g., That was the hardest thing too see), the final generated story doesn't have any persona-related event. These errors also indicate the target sentence plays an important role in controlling the protagonist's persona in story generation.
Persona	Generated Stories	PC	Rouge-2
You have a loose grasp on your emo-tion and are quick to lash out. Often hurting the ones you care about most.	...She had started to try to get her emotions un-der control, she had tried to keep herself calm dur-ing all of this, and she had only gotten worse. She stood, just standing, and was staring at the class like an idiot, her head down...	0.90	0.0
In command of some measure of magical power	...Wilhelm is in command of all forces in the north. After the war ended, his mind was full of food and drink, and he was ready for a quick trip to the bar. He had been to the bar on many occasions. Only in there that he could fully relax and forget about all those mess things...	0.16	0.14
Table 12: Typical cases by PC (Persona-Consistency) score. Bold words denote the consistent events adhering to the given persona. The overlapping words are underlined.
H Discussion of the Persona-Consistency Metric
To measure whether the generated story is consistent with the given persona, we propose the Persona-Consistency(PC) metric. In our experiments, we replace the ground-truth story with a randomly sampled one to construct a inconsistent story-persona pair as a negative sample. The finetuned classifier achieves an  $83.63\%$  accuracy on the auto-constructed test set. However, it is possible that the PC metric depends on the word overlap to make predictions because of the simple random sampling of the negative samples (Lin et al., 2020). We thus conduct a case study to investigate whether our PC metric depends on word overlap to make judgments. As shown in Table 12, the first example gets a high PC score since the story embodies a consistent persona with the given persona description, in spite of a low rouge score. In contrast, the second example has an overlapped phrase "in command of" with the persona description but does not embody the corresponding persona description, and thus gets a high rouge score and low PC score. The results show that PC may not depend on shallow features like word overlap to make judgments.
What's more, we have taken into account the shortcomings of the automatic metrics for NLG and thus additionally added the human evaluation to further prove the effectiveness of our method.
Figure 3: The guideline of story quality evaluation.
Error Type	Cases
Opposite	Context: The students have a surprisingly restful night of sleep. No weird dreams, no monsters, no strangers in tuxedos. The students head out to school. They run into each other as they enter the building and decide to have a chat about yesterdays events over breakfast...The students grab what the want to eat and sit at a table near the back of the cafeteria. They agree to try to keep calm in order to not raise the suspicion of other students and faculty.
Persona: [Vivian] You are frequently lost in thought making you slow to action.
Story: ...It's a big space, so that's a great opportunity to get excited, to get to know some of the new people. You may not be the most social person around, but you're here for fun...”
Plan keywords: excit→student→like→come→...
Irrelevant	Context: The lights are bright above you, you lay in a bed with metal bars on either side. At first you think you are in a hospital but it did not smell like one, in fact you could smell nothing at all. When you lift your head you see that there are seven other people in the room with you and you are all wearing white clothing that looks a lot like scrubs but made of much better fabric...There is a doorway that leads into a hall, there is no door just an open doorway and the hall looks to be the same as the room.
Persona: [Shay Florence] You are not anxious and it is easy for you to believe someone else
Story: What's going on here? ... Looking up the hall, the room had black or gray light. The dark looked like that from the darkest rooms in the world, or maybe it could be dark, darker. I could see it from the window, the darkness in the room, even the bed, but that couldn't be the case. There was nothing here. There was just a room with no light. ... I had to take the other side to the hall, to the other side of the hall. The hallway was endless. That was the hardest thing to see...
Plan keywords:room→bed→nightmare→wall→hallway→wake...
Figure 4: Typical errors by our model. Bold words indicate the events exhibiting the opposite persona. Italic words indicate the events that are irrelevant with the given persona. And the red sentence indicate the generated target by CONPER.