Machine Narrative Comprehension in Fictional Characters Personality
Prediction Task
Yisi Sang
Syracuse University

Xiangyang Mou
Rensselaer Polytechnic Institute

Mo Yu
WeChat AI

Dakuo Wang
IBM Research

Jing LiNew Jersey Institute of Technology

Jeffrey Stanton
Syracuse University

Abstract
An NLP model that understands stories should
also be able to understand the characters,
which is underexplored till now. To support the development of neural models forthis purpose, we construct a benchmark,
Story2Personality. The task is to predict a movie character’s personality based on
the narratives. Experiments show that our task
is challenging for the existing text classification
models, as none is able to largely outperform
random guesses. We then proposed a multiview model for personality prediction using
both verbal and non-verbal descriptions, whichsignificantly improved the performance. The
uniqueness and challenges in our dataset callfor the development of narrative comprehension techniques from the perspective of understanding characters.1

1

Introduction

Understanding characters in a story is a fundamental human cognitive capability according to
psychology and education theories (Bower andMorrow, 1990; Paris and Paris, 2003; Xu et al.,2022). The NLP community has limited workon machine’s character comprehension capability,
but most of the existing studies focus on short or
expository text snippets (e.g., story summaries orfragments) (Urbanek et al., 2019; Brahman et al.,2021; Sang et al., 2022a). Moreover, most ofthem are limited on the superficial “understanding” (more like information retrieving) of characters, such as coreference resolution (Chen and Choi,
2016) and character relationship extraction (Iyyer
et al., 2016). Few studies have explored the actual comprehension of characters, such as from thepersona (Flekova and Gurevych, 2015; Sang et al.,2022b) perspective, which is how humans understand a character and build connections with it in a
1
Our code and data are released at https://github.
com/YisiSang/Story2Personality

Figure 1: An example excerpt from the movie script of “The
Matrix”. Blue utterances are the character Morpheus’s scene
descriptions; Red are his dialogues. Morpheus’s personality
was rated as ENFJ by 300 user votes.

book reading (Xu et al., 2021) or movie watchingexperience.
We propose Story2Personality, a new
narrative understanding benchmark to enable newresearch opportunities of character understanding.
The task of Story2Personality is to predict
personality according to the character’s narrative
texts in the script. We focus on the The Myers–Briggs Type Indicator (MBTI) (Myers, 1962)
personality type that assess the psychological preferences in how people perceive the world and makedecisions in four categories: introversion or extraversion, sensing or intuition, thinking or feeling,
judging or perceiving.
Personality prediction from narratives has many
challenges. First, stories often use a variety ofnarrative clues (e.g., scenery changes),sequence(e.g., flashback) and rhetorical techniques (e.g.,
metaphor). Second, the inputs of the task are long(>10K words on average), challenging the applications of Transformer-based models (Vaswani et al.,2017). Third, both the scene descriptions and dialogues are informative for the prediction, requiringmodels to jointly consider multi-view of inputs.
We make the following contributions:• We establish a large-scale dataset for personality
prediction of narrative characters (3,543 characters from 507 movies with 4-dimensions MBTI
label). Our dataset is proved challenging — on
this binary classification task, none of the baselines achieve higher than 60% macro-F1.
• We develop a trainable movie script parser to automatically process a script to a structured form
with the verbal dialogues and the non-verbal
scene descriptions illustrating backgrounds. Human study shows that our parser is more accuratecompared to previous rule-based tools.
• Inspired by psychological theories (McCroskey
and Richmond, 1996; Richmond et al., 2008), we
propose an extension to BERT classifier (Devlin
et al., 2018) to handle the long and multi-view(verbal and non-verbal) inputs. Our model improves 2-3% over the baselines.

2

Related Work

Character-Centric Narrative Understanding
There have been a few existing studies on charactercentric machine narrative understanding, but only
work on summaries of stories or summaries ofcharacters (Massey et al., 2015; Srivastava et al.,2016; Brahman et al., 2021). Thus, they do notneed to handle the long narrative inputs as how
humans read a narrative. Some other works consider long narratives as input but focus only on
extracting inter-character relationship via counting
co-occurrence (Elson et al., 2010; Elsner, 2012;
Iyyer et al., 2016; Chaturvedi et al., 2016, 2017;
Kim and Klinger, 2019).
For our goal of understanding characters fromnarratives, we rely on fundamental NLP techniques
for books and screenplays, such as named entity
recognition (Bamman et al., 2019), coreference
resolution (Chen and Choi, 2016) and entity-centric
natural language modeling (Clark et al., 2018).
The most relevant work to ours is about latentpersona induction (Bamman et al., 2013). The
work learns a topic model over character behaviors
from books, and then consider or assume each latent topic corresponds to an induced persona. The
induced persona vectors can be then applied topotential applications as a type of character representation, but they did not have psychologicaltheory behind the assumption.

Background of MBTI Personality is a “stable
and measurable” individual characteristic (Vinciarelli and Mohammadi, 2014) which can “distinguish internal properties of the person from overt
behaviors” (Matthews et al., 2003). MBTI and
the Big-5 Personality are two of the most popularpersonality scales. We choose MBTI as the annotation criteria as research shows that a person’sfriend can accurately judge his/her MBTI personality (i.e., third-person judgement validity) (Cohenet al., 1981). In our narrative comprehension scenario, a fictional character’s MBTI personality is
judged by other human watchers (third person),which should yield a reasonable validity.
MBTI has four dimensions. E/I: extravert (E) isseen as being generally active and objective while
the intravert (I) is seen as generally passive and subjective (Sipps and Alexander, 1987). S/N: sensing(S) is seen as attending to sensory stimuli; intuition(N) describes a more detached, insightful analysis
of events and stimuli (Boyle, 1995). T/F: thinking(T) involves logical reasoning and decision making;feeling (F) involves a more subjective and interpersonal approach (Thomas, 1983). J/P: judging (J)attitude is associated with prompt decision making;perception (P) involves greater patience and waiting for more information before making a decision.
An individual’s MBTI type has a label based onher dominant preference for each dimension (e.g.,Morpheus from “The Matrix” is ENFJ in Figure 1).

3

Story2Personality Dataset

We constructed our dataset in three stages: extracting movie scripts from the Internet Movie Script
Database (IMSDB 2 ), parsing the collected movie
scripts into dialogue and scene sections, matching
characters’ personality types from The Personality
Database(PDB3 ) with their dialogues and scenes.
3.1

Movie Scripts Collection

We crawled HTML files from IMSDB combinedwith movie scripts in NarrativeQA (Kočiskỳ et al.,2018). After removing corrupted or empty files, wegot 1,464 usable movie scripts.
3.2

Our Statistical Movie Script Parser

As shown in Figure 1, a movie script usually
has four basic format elements (Riley, 2009):
Scene Headings, one line description of each23

https://imsdb.com/
https://www.personality-database.com/

Rule-Based Pre-Processing We start with a rule
to classify the sections into dialogues and scenes.
As Figure 1 shows, a common format of movie
scripts is to align the shot headings, transitions
and scene descriptions vertically, and uses a larger
indentat for dialogues. So, the indent size can beused to identify dialogues. Since the indentat size
may vary across different scripts. Our rule assumes
the sections as dialogues if they have larger indentcompared to FADE IN in the same script and theothers as scenes.
Silver Parses Construction The rule-based preprocessing introduces many noises. We then designed a statistical method to automatically determine the threshold indent of dialogues. First, we
compute the averaged ratio µ of dialogues in ascript and its standard variation σ. Second, we keep
adding sections with the largest indent sizes to theset of dialogues, until the ratio of added sections
becomes larger than µ+σ. Finally, we keep theleft sections as scenes. If none of the indentation
size can reach the ratio of dialogues in the range ofµ±σ, the movie script was seen as a failure case.
We designated the successfully processed scripts
with the dialogues/scene labels as the “silver” setwhich consists of 29% of the scripts.
Section Classifier For the failure scripts from theprevious step and the scripts without FADE IN
markers, we trained a BERT-based section classifier using 137,042 labeled sections from the silver
set to label them. The classifier achieved 99.31%
accuracy on a held out validation set. The outputs
are our final parses.
3.3

Personality Collection and Mapping

We collect human rated MBTI types from PDB.
Movie scripts are the blueprint for the actor’s performance. An actor’s body language, dialogue,

(a)
(b)

scene’s type, location, and time (i.e., INT. ROOM
1313); Scene description, the description of theactions of the characters (i.e., text in blue); Dialogues, names of characters and actual words they
speak (i.e., text in red); Transitions, instructions
for linking scenes together (i.e., FADE IN ON).
In order to extract dialogues and scene descriptions in a structured form, we first split the scripts
to sections, i.e., text chunks between two adjacentbolded chunks which are scene headings or character names and stored the bolded texts as section
titles. Then we designed a statistical method toclassify the section types:

Dimension

Train(%)

Dev(%)

Test(%)

E/I
N/S
T/F
J/P

45.9/51.8
36.6/60.4
54.7/43.2
46.4/51.3

49.6/49.0
41.8/54.0
45.8/50.8
47.2/51.2

52.6/44.2
41.4/55.0
46.0/52.8
45.6/53.0# dialogues/character# words/dialogue# scenes/character# words/scene

Mean

Min

Max

76.90
917.7441.08
1,381.47

0
1
0
1

776
12, 536495
25,457

Table 1: Distribution of two personality types per dimension(a) and core statistics (b) in Story2Personality.

and contexts are all described in the scripts (Jhala,2008). Human rater’s perception of a character’s
personality from the movies would be consistent
with the script’s description. In total, we collected
MBTI types of 28,653 characters. Each characterhas an id, name, vote count, and voters’ agreement
on each MBTI dimension. For example, the MBTI
profile in Figure 1 has 300 voters, with different
agreement rate along each dimension. To ensure
the quality of personality voting, we removed character profiles with <3 voters and <60% agreement
rate so some characters do not have all the 4 dimensions. When the user starts rating, the rating interface hides the previous rater’s choices. Thus, therater would not have prior bias. We then matched
the characters’ personality profiles to the scripts,
if the name can be softly matched to the dialogue
title or the recognized named entities in the scenes.
Table 1 shows the core statistics of our dataset.
3.4

Statistics of Human Agreement

Table 2 lists the human agreement score on each
MBTI dimension, on which we compute the humanaccuracy and approximate human macro-F1 scores.
The raters are most divided in annotation of N,with an average agreement is 91.06% and the standard deviation 0.11. One reason is that the perceptual style dimension N/S measures how the individual obtain information. Comparing with dimensions related to attitudes (E/I) or decision making(T/F, J/P) (Jung, 2016) perceptual style is more
implicit. Specifically, S is seen as attending to sensory stimuli, while N describes a more detached,insightful analysis of events and stimuli (Boyle,1995). They are more difficult to determine fromthe explicit story narratives.

I
E
N
S
T
F
P
J

Mean

Min

Max

STD

#Character

94.43%
94.22%
91.06%
93.32%
94.22%
93.68%
93.68%
93.72%

60%
60%
60%
60%
60%
60%
60%
60%

100%
100%
100%
100%
100%
100%
100%
100%

0.10
0.10
0.11
0.11
0.10
0.10
0.10
0.10

1,783
1,679
1,347
2,082
1,851
1,617
1,825
1,644

Table 2: Descriptive statistics of voters’ agreement
Correct scene

Correct dialogue

85%
97%

93%
100%

Ramakrishna et al. (2017)
Our parser

Figure 2: Multi-row multi-view BERT model architecture.

Table 3: Comparison of correct parsing results.

4

Dataset Analysis

Script Parsing Results We compared our parsing
results with the results of the state-of-the-art opensourced script parser (Ramakrishna et al., 2017),
which employs many human written rules, with ahuman study. We randomly selected five scene descriptions and five dialogue sections in 10 common
movies, giving 100 snippets for evaluation (40 fromthe silver set). Then we manually compared theparsing results with the original movie scripts. Table 3 shows the results. Our parser outperforms Ramakrishna et al. (2017) with a large margin. Most
mistakes of (Ramakrishna et al., 2017) is to recognize scenes as dialogues. There are other parsers
but did not publish the code or data, so we cannot conduct human study for comparison. A stateof-the-art learning model (Agarwal et al., 2014)
reports 91% accuracy on line-level classification.
In a preliminary study, we achieve 99% on thistask, but finally choose do conduct more accurate
section-level classification as in Section 3.2.
Human performance We take the majority voteof each character’s MBTI types as the groundtruth.
This gives an averaged 93.54% human accuracy
across the four personality dimensions on our test
data. Computing humans’ macro-F1 score lacks ananalytical form from the agreement scores. Therefore we make an approximation by sampling threevoters (the minimum number of voters in ourdataset) for each character and treating them likethe predictions of three different models. This gives
overall >95% scores which is much higher thanmodel performance (in Table 6). The statistics ofhuman agreement on MBTI dimensions is shownin Table 2. Table 4 lists the distribution of all the

16 MBTI types in our dataset, together with a representative movie character for each type.
Personality%

Example

ISTJ
ISTP
ESTP
ESTJ
ISFJ
ISFP
ESFJ
ESFP
INFJ
INFP
ENFP
ENFJ
INTJ
INTP
ENTP
ENTJ

8.41%
8.07%
8.21%
6.52%
6.41%
6.49%
4.88%
7.06%
4.80%
5.42%
3.90%
3.75%
4.26%
3.75%
4.94%
4.88%

Darth Vader (“Star Wars”)
Shrek (“Shrek”)
Han Solo (“Star Wars”)
Boromir (“The Lord of the Rings”)
Forrest Gump (“Forrest Gump”)
Harry Potter (“Harry Potter”)
Cher Horowitz (“Clueless”)
Jack Dawson (“Titanic”)
Edward Cullen (“Twilight”)
Amélie Poulain (“Amélie”)
Anna (“Frozen”)
Judy Hopps (“Zootopia”)
Michael Corleone (“The God Father”)
Neo (“The Matrix”)
Tyler Durden (“Fight Club”)
Patrick Bateman (“American Psycho”)

Table 4: Distribution of the 16 MBTI personality types
in Story2Personality

5

Experiments

Baselines We build two baseline models.
•SVM, the LinearSVC from sklearn.svm. We
extracted top 20K word unigram, bigram, and trigram features according to term frequency after
removing stop words. We set C=0.1.
• BERT, fine-tuning the out-of-box BERT, witha linear head on the ‘[CLS]’ token’s final layer
embedding for classification.
Our Method We propose the multi-view multirow BERT (MV-MR BERT) classifier (Fig. 2)
which is an extension of BERT to deal with thelong inputs and handle the verbal and non-verbal
information differently. First, to handle the long
input per character, we borrow the idea from fusion-

1K
SVM
BERT

50.33
54.32

1.5K
52.19
55.42

2K
54.56
55.58

∼2.5K

Model

E/I

N/S

T/F

J/P

55.41
55.59

SVM
BERT
MV-MR BERT
- multiview

54.65
56.06±0.73
57.50±2.04
57.30±1.91

55.41
55.59±3.36
57.42±4.27
57.05±1.80

52.83
57.13±0.9760.33±0.93
57.04±2.05

56.18
57.59±1.40
59.83±1.42
57.39±2.21

Human Perf.

98.19±0.60

97.82±0.10

98.51±0.67

98.03±0.19

Table 5: Learning curve on N/S.

in-decoder (Izacard and Grave, 2020). Since thecomplexity of Transformers is O(RL2 ) (with R thenumber of rows and L the length per row), when L
is very large, we can split it into multiple segments
to reduce the quadratic term. Next, we rely on theattention over all the segments to fuse the information. Specifically, we split the input content D of acharacter into multiple segments D = {Si }R
i=1 ,and encode all the segments in a minibatch as
H = BERT(Si ) ∈ RR×L×d , where d is the hidden
state size. Then a linear head is applied to get theattention score across tokens in all the rows as α =softmax(HW + b) ∈ [0, 1]R×L . The final summarized representation of thePinputP
D is thus theRLweighted summation hD =i=1
j=1 αij Hij .
Second, to handle both the dialogue and behavioral description a character, our multi-view model
receives an input pair (Ddial , Dscene ), then uses ashared BERT and separated linear heads to comscene
pute the summarized states hdial
D and hD . The
two vectors are fed into a fully-connected layer forprediction. For the scene descriptions, we prepend
a special token “[ent]” to the target character’sname to denote its position. The attention αscene isonly computed on these special tokens.
Results and Analysis Following Flekova andGurevych (2015), we use macro-averaged F1 asevaluation metric. Table 6 shows the main resultson the four MBTI dimensions. Peak performance
was achieved by our MV-MR BERT. The result suggests using both dialog and action scene descriptions consistently improved model performance.
The results are generally low compared to human performance, showing the task is challenging
to existing models. We analyzed the learning curveof BERT model by adding the training data from
1K to 2.5K characters (Table 5). The model performance did not change a lot in the development
dataset. Figure 3 gives further evidence for thechallenge of our task, which shows the dev andtest results are not highly-correlated, meaning thatby achieving near perfect accuracy on the training
data, the models largely overfit the noises insteadof capturing real clues.
Figure 3 gives further evidence for the challenge

Table 6: Macro F1 scores on the four dimensions

Figure 3: Dev vs. test F1 scores of BERT-based models.
J/P
T/F
N/S
I/E

0.5K

1K

2K

4K

59.79
59.91
56.15
61.64

58.18
64.31
53.86
61.05

60.35
63.32
55.65
62.87

58.77
65.42
57.18
62.69

Table 7: Ablation experiment on input length.

of our task, which plots the dev versus test scores
during our model selection. It shows the dev andtest results are not highly-correlated, meaning thatby achieving near perfect accuracy on the training
data, the models largely overfit the noises insteadof capturing real clues. Both length and multiview
have an improvement on model performance, butlength has a slightly smaller impact, as shown in Table 7, when increasing the number of input tokens,the performance is not greatly affected.

6Conclusion

We develop a movie script parser and proposed a new narrative understanding benchmark,
Story2Personality, which enables neuralmodel training for understanding characters. We
evaluate several classifiers on our task – while ourmulti-view multi-view BERT model achieves a substantial improvement over the SVM and BERT
baselines, there is a huge gap compared to humanperformance. This indicates our dataset a valuable
and challenging task for future research. In the
future, we will continue to expand our dataset andbuild downstream applications.

Acknowledgements
This research was supported, in part, by the NSF(USA) under Grant Numbers CNS–1948457.

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