# Stage Direction Classification in French Theater:
## Transfer Learning Experiments

Alexia Schneider, Pablo Ruiz Fabo

► To cite this version:

Alexia Schneider, Pablo Ruiz Fabo. Stage Direction Classification in French Theater: Transfer Learning Experiments. Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), Association for Computational Linguistics, Mar 2024, Saint-Julian's, Malta. pp.278-286. hal-04520702

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# Stage Direction Classification in French Theater:
## Transfer Learning Experiments

Alexia Schneider and Pablo Ruiz Fabo

Université de Strasbourg, LiLPa UR 1339
67000 Strasbourg, France

alexia.schneider4@etu.unistra.fr, ruizfabo@unistra.fr

## Abstract

The automatic classification of stage directions is a little explored topic in computational drama analysis (CDA), in spite of their relevance for plays’ structural and stylistic analysis. We developed a 13-class stage direction typology, based on annotations in the FreDraCor corpus (French-language plays), but abstracting away from their huge variability while still providing classes useful for literary research. We fine-tuned transformers-based models to classify against the typology, gradually decreasing training-corpus size to compare model efficiency with reduced training data. A result comparison speaks in favour of distilled monolingual models for this task, and, unlike earlier research on German, shows no negative effects of model case-sensitivity. The results have practical relevance for computational literary studies, as comparing classification results with complementary stage direction typologies, limiting the amount of manual annotation needed to apply them, would be helpful towards a systematic study of this important textual element.

## 1 Introduction

Machine learning methods have brought important contributions to Computational Literary Studies (CLS). To name just one monograph-length work, Underwood (2019) used such methods to provide insights on complex issues like the long-term evolution of genres and of literary prestige criteria, focusing mainly on fiction and poetry. Drama has also benefited from such approaches. The recent Computational Drama Analysis workshop<fnref n="1" /> featured work on the automatic classification of dramatic situations, character types, and emotions in drama. In this paper, we approach a little explored dramatic analysis topic: the automatic classification of stage directions, using a French theater corpus. Stage directions introduce indications about performance, decoration or other information to

complement character speech, but can sometimes be largely independent from it (Pfister, 1988, 15). Several typologies have been proposed for them in literary studies (see 3.1). However, their automatic classification has scarcely been studied and poses challenges, given types hard to distinguish from each other. Stage directions on characters’ entrance and exit indicate changes to character coexistence on stage and are thus tied to play structure and dramatic technique. The frequency and length of stage directions and their types can be stylistic parameters related to author groups or sub genres. Automatically classifying stage directions facilitates large-scale quantitative analyses of this element’s structural and stylistic role.

For French theater, the FreDraCor corpus (Milling et al., 2021), based on Fièvre (2007) and covering mostly the 16th to 20th centuries, offers over 38,000 annotated stage directions. Given the large number of categories (over 5,000), exploiting these annotations for supervised learning is a challenge, that we address in the paper.

Pre-trained language models were a game changer in NLP, allowing for transfer learning (e.g. Devlin et al. 2019) that yields viable classifiers even with a reduced number of examples, or, in the case of larger language models (Brown et al., 2020), in-context learning from (almost) zero examples. In our study, we work with specialized categories for which we could develop annotations, and we opted for transfer learning. We examine the extent to which we can reduce manually annotated training data for the supervised classification of stage directions, in French. As producing manual annotations can be costly, and exploring literary questions may require comparing the results of classifying against several, complementary typologies, the question addressed has practical relevance for CLS. The paper’s contributions are:

* A new stage direction typology (3.1) based on the related literary theory and on the FreDra-

<footnote n="1">¹https://page.hn/anuvah</footnote>
Cor types, but abstracting away from some
of their large variability to obtain a category
set amenable to supervised learning and still
useful for addressing literary questions.

* Experiments to clarify which language models (LMs) learn most efficiently on such data, focusing on model characteristics that may generalize beyond our corpus language (French).

An overall goal is to start a reflection on good practices to develop methods to classify this textual element, across languages.

The paper is structured as follows: Section 2 reviews related work. Section 3 describes our typology and classification workflow. Section 4 presents results, and section 5 outlines future perspectives.

## 2 Related Work

Heterogeneous criteria have been used to design stage direction typologies (Dahms, 1978; Gallèpe, 1997; Issacharoff, 1981; Martinez Thomas, 2007; Pfister, 1988). Among other aspects, taxonomies pay attention to whether stage directions refer to verbal/speech-related or visual information, to characters or setting, whether they describe movements (including entrance and exit) or character interactions. Another feature used is their narrative vs. descriptive nature, their relatedness with or independence from spoken text, or their impact on the play's plot. The Text Encoding Initiative (TEI) guidelines (TEI Consortium, 2023) reflect this heterogeneity in their description of possible @type attribute values for the TEI <stage> element, used for stage directions. Galleron (2021) attempts to reconcile inconsistencies in earlier typologies, systematizing types via a set of values for the @ana attribute of <stage>.

Automatic stage direction classification was performed in German by Dennerlein (2016), with four classes (exit, entrance, dead and aside); per-class results ranged between 0.75 and 0.88 F1 using random forests. The typology is less complex than our 13-class typology and the classes are more distinct. Maximova and Fischer (2019), working with Russian, developed a model to classify against the TEI guidelines' 9 categories, trained on 6,569 manually annotated examples and reaching ca. 0.75 F1.

Pagel et al. (2021) performed a related but not equivalent task. In their study on predicting German plays' structural elements in TEI, one of the five classes was stage directions, besides act and

scene divisions, speaker names and speeches. They thus classified stage directions vs. other structural elements, reporting that a binary classification between stage directions and character speech was not trivial (0.81 F1). Stage directions were also the worst performing class in the 5-way classification (0.84 F1, while other classes were above 0.9). To assess the role for language-specific knowledge, they fine-tuned both English and German BERT cased and uncased models, with best results for the German uncased model. Their experimental setup informed our own (3.2).

## 3 Methods

### 3.1 Stage Direction Typology

We wanted to start assessing to what extent it is possible to automatically classify stage directions against different typologies useful for literary analysis. We do not intend the typology here to be the only choice, but in a way a testbed for fine-tuning and a means to assess the potential of the models to classify this type of material, using these classes or similar ones according to researchers' needs.

To develop the typology (table 1),<fnref n="2" /> we started off from FreDraCor, which has 38,306 <stage> elements with 5,109 unique types.

We grouped semantically the 87 most frequent values, covering 25,823 stage directions, into our 13-class typology, creating a mapping between FreDraCor original labels and our own (Appendix B). E.g. FreDraCor labels *location*, *decor* and *décor* yield class *Setting* in our typology, and *kill*, *fight*, *hit*, *suicide*, *threat* yield *Aggression* in our typology. We only considered single-type FreDraCor labels, for simplicity; the 87 categories selected correspond to classes with at least 50 examples.

The typology contains classes that can be very ambiguous, the vocabulary of which is likely to represent different semantic fields, like *Action* or *Narration*, which are intended to be difficult for classifiers. Other classes can often be detected with surface lexical cues, like the presence of certain prepositions in *Toward*. Our choice of classes is meant to reflect different interests that a scholar studying stage directions may have. E.g. *Aggression* stage directions may be more present in a serious subgenre like the tragedy, *Music* stage directions in the *vaudeville*, or long *Narration* types in plays from the 19th century onwards or experimental work. Thus, the detection of such types is

<footnote n="2">²See corpus examples and English glosses in appendix A.</footnote>
<table><caption>Table 1: Stage direction typology</caption><thead><tr><th>Class</th><th>Scope</th></tr></thead><tbody><tr><td>Action</td><td>General character action category</td></tr><tr><td>Aggression</td><td>Violent action</td></tr><tr><td>Aparté</td><td>Aside (character addresses audience or is alone)</td></tr><tr><td>Delivery</td><td>Delivery manner (e.g. laughs, sobs)</td></tr><tr><td>Entrance</td><td>Character enters stage</td></tr><tr><td>Exit</td><td>Character exits</td></tr><tr><td>Interaction</td><td>Non-verbal character interaction</td></tr><tr><td>Movement</td><td>Character movement (but not exit/entrance)</td></tr><tr><td>Music</td><td>Tune names (in plays with songs)</td></tr><tr><td>Narration</td><td>Long, “narrative quality”, for readers</td></tr><tr><td>Object</td><td>Describes object or interaction with it</td></tr><tr><td>Setting</td><td>E.g. the stage represents a bar</td></tr><tr><td>Toward</td><td>Indicates the addressee of a speech</td></tr></tbody></table>

relevant for subgenre characterization. Some types are relevant for dramatic structure, e.g. *Exit* and *Entrance*, which give information about configuration (character co-presence on stage). *Aparte* is related to knowledge distribution in the play, an active CDA research area (Andresen et al., 2024).

After removing duplicates, we obtained a set of 14,613 examples and used it, gradually decreasing training set size, for our fine-tuning experiments (3.2). Label distribution is imbalanced (table 2).

<table><caption>Table 2: Number of examples per class (training corpus)</caption><thead><tr><th>Class</th><th>N. examples</th><th>Class</th><th>N. examples</th></tr></thead><tbody><tr><td>Music</td><td>2863</td><td>Delivery</td><td>962</td></tr><tr><td>Action</td><td>2467</td><td>Entrance</td><td>646</td></tr><tr><td>Toward</td><td>2144</td><td>Movement</td><td>583</td></tr><tr><td>Exit</td><td>1295</td><td>Interaction</td><td>565</td></tr><tr><td>Object</td><td>1130</td><td>Narration</td><td>554</td></tr><tr><td>Setting</td><td>982</td><td>Aggression</td><td>350</td></tr><tr><td></td><td></td><td>Aparté</td><td>72</td></tr></tbody></table>

## 3.2 Classification Workflow

We first implemented classical machine learning (ML) models, with version 1.0.2 of scikit-learn (Pedregosa et al., 2011): Logistic Regression, Ridge Classifier, Random Forest and SGD. For the last two, results reported (macro-F1) are averaged over 5 runs. The implementation used for the first two is deterministic. Hyperparameters were default. The features were character length and the number

of sentences, tf-idf-weighted token unigrams, bigrams, and part-of-speech unigrams (obtained with the fr_core_news_md module of spaCy by Honnibal et al. 2020) and 2- to 4-character ngrams, with 1% minimum document frequency.

Models for fine tuning were (1) camembert-base (Martin et al., 2020), a French monolingual model trained on the OS-CAR corpus (Ortiz Suárez et al., 2019); (2) distilcamembert-base (Delestre and Amar, 2022), a version of (1) that is distilled, i.e. that attempts to preserve result quality while reducing model complexity (thus size and fine-tuning time); (3) bert-base-multilingual-cased and (4) bert-base-multilingual-uncased, case-insensitive version of (3), both by Devlin et al. (2019), which include French among the 102 languages covered. Model (5) was distilbert-base-multilingual-cased (Sanh et al., 2019), a distilled cased version of (3). The final model (6) was based on the SetFit architecture (Tunstall et al., 2022). This first fine-tunes a Sentence Transformer (S-BERT) model (Reimers and Gurevych, 2019) using contrastive training on positive and negative training pairs, which helps it learn effectively based on a small number of examples. Then, based on the fine-tuned S-BERT model (distiluse-base-multilingual-cased-v1 in our case), it trains a classifier for the task, with logistic regression in our setup. Fine-tuning was carried out with version 4.34.1 of the transformers library (Wolf et al., 2020) on a V100 GPU. Learning rate was 2e-5 and batch size was 16. The trainer was configured with a maximum of 40 epochs, with early stopping monitoring validation loss and a patience of 3, but only camembert-base went over 10 epochs on average. For SetFit, as its API has no early stopping callbacks, we chose 6 epochs with 20 contrastive learning iterations each. We report macro-F1 (mean over 5 runs) in table 4.

Our model choices are justified thus: CamBERT (1) is a leading monolingual LM for French. A distilled version (2) was also tested because, given that distilled models are smaller and faster, should there be no important difference in result quality, the distilled model is to be preferred. The multilingual BERT models, cased (3) and uncased (4) were chosen to compare with the monolingual ones, to get an indication of the extent to which language-specific knowledge helps classifi-
cation, especially as training data is reduced. We chose the distilled version (5) for the same reason as (2). Finally, we tested SetFit (6) because its contrastive learning approach allows it to learn from limited data, which fits our study's goal. For our experiments, the annotated corpus was increasingly reduced (table 3), and split into training and validation sets. The test-set was always the same (2,923 examples). This was meant to help assess the extent to which models generalize to the test set, even when fine-tuned on a training set smaller than it.

<table><caption>Table 3: Number of examples at each corpus size</caption><thead><tr><th></th><th>100%</th><th>50%</th><th>25%</th><th>10%</th><th>5%</th></tr></thead><tbody><tr><th>train</th><td>9352</td><td>4676</td><td>2337</td><td>935</td><td>467</td></tr><tr><th>validation</th><td>2338</td><td>1169</td><td>585</td><td>234</td><td>117</td></tr><tr><th>test</th><td colspan="5">2923</td></tr></tbody></table>

# 4 Results and Discussion

As table 4 and figure 1 show, LMs performed better than classical ML; improvement increases as training set size decreases. With the full corpus, several LMs reach 0.81 F1, and at 50% (5,845 manually annotated examples), macro-F1 ranges between 0.73 and 0.79. Monolingual LMs show an advantage over multilingual ones from 50% on, more so at 25% (2,922 training examples), 10% (1,169 examples) and 5%. Distilled models performed very closely to full ones, either in mono- or multilingual cases. As their fine tuning was taking between 40% and 75% of the time needed by the full model, they are a better choice. SetFit is the only case where a multilingual and distilled model was competitive with monolingual models at 10% (only 0.01 points below distilled Camembert), which suggests the effectiveness of its contrastive learning approach. However, fine-tuning time with our setup was much higher than with all other models.

We see no consistent difference between cased vs. uncased models, contrary to Pagel et al. (2021), who observed that a cased model may generalize less well on their German data. The relevance of language-specific knowledge seen in their study is also seen in ours with the higher performance of French language-specific models. The 10% results are interesting, as they were obtained with less than 1,200 manual annotations. The monolingual models provide ca. 0.7 F1 (vs. approx. 0.8 F1 with ca. 6,000 examples). A 0.7 F1 may not be

<table><caption>Table 4: Macro-F1 with different training-set sizes, testing on the 2,923 example test-set</caption><thead><tr><th>Model</th><th>100%</th><th>50%</th><th>25%</th><th>10%</th><th>5%</th></tr></thead><tbody><tr><td colspan="6">Classical ML</td></tr><tr><td>LogisticReg</td><td>0.70</td><td>0.64</td><td>0.57</td><td>0.51</td><td>0.41</td></tr><tr><td>RidgeClassif</td><td><strong>0.73</strong></td><td><strong>0.71</strong></td><td><strong>0.62</strong></td><td><strong>0.55</strong></td><td><strong>0.49</strong></td></tr><tr><td>RandomForest</td><td>0.65</td><td>0.61</td><td>0.57</td><td>0.49</td><td>0.45</td></tr><tr><td>SGD</td><td><strong>0.73</strong></td><td>0.69</td><td><strong>0.62</strong></td><td>0.54</td><td><strong>0.49</strong></td></tr><tr><td colspan="6">Transfer learning</td></tr><tr><td>(1) cam-base</td><td>0.77</td><td>0.77</td><td><strong>0.75</strong></td><td><strong>0.71</strong></td><td><strong>0.68</strong></td></tr><tr><td>(2) d-cam-base</td><td><strong>0.81</strong></td><td><strong>0.79</strong></td><td>0.74</td><td>0.7</td><td>0.67</td></tr><tr><td>(3) mbert-cas</td><td><strong>0.81</strong></td><td>0.74</td><td>0.69</td><td>0.61</td><td>0.51</td></tr><tr><td>(4) mbert-unc</td><td><strong>0.81</strong></td><td>0.74</td><td>0.7</td><td>0.61</td><td>0.57</td></tr><tr><td>(5) d-mbert-cas</td><td>0.8</td><td>0.73</td><td>0.67</td><td>0.61</td><td>0.53</td></tr><tr><td>(6) setfit-dmc</td><td>0.78</td><td>0.74</td><td>0.69</td><td>0.69</td><td>0.62</td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>Figure 1: LM performance as training set is reduced</figcaption></figure>

enough for a number of literary studies questions. However, an iterative workflow may be attempted with manual correction of the model outputs and fine-tuning of a better model while keeping the number of manual annotations low. Manual corrections could focus on the worst performing categories. A possible use of the best models would be to automatically annotate FreDraCor stage directions beyond the most frequent 87 single-type labels, which we did not handle here (section 3.1) and account for ca. 30% of the corpus, to assess whether the model's predictions could be a viable way to simplify the corpus' wide variety of labels. The same could be done to corpus examples that bear mixed-type labels.

Confusion matrices for distilled Camembert fine-tuned on 100% and 10% of the data are in figure 2,<fnref n="3" /> and per-category results in table 5. Cate-

<footnote n="3">³Best F1 of 5 runs; Std Dev 0.012 at 100%, 0.009 at 10%.</footnote>
<figure><img src="image_3.png" /><figcaption>(a) distilcamembert-base, 100% of data</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) distilcamembert-base, 10% of data</figcaption></figure>

<table><caption>Figure 2: Confusion matrices for distilled Camembert. Values are normalized. The right column shows number of examples per class</caption><thead><tr><th rowspan="2">Class</th><th colspan="3">FT on 100% of data</th><th colspan="3">FT on 10% of data</th><th rowspan="2">N</th><th rowspan="2">Diff</th></tr><tr><th>P</th><th>R</th><th>F1</th><th>P</th><th>R</th><th>F1</th></tr></thead><tbody><tr><td>Action</td><td>0.907</td><td>0.844</td><td>0.874</td><td>0.821</td><td>0.829</td><td>0.825</td><td>486</td><td>-0.049</td></tr><tr><td>Aggression</td><td>0.713</td><td>0.827</td><td>0.765</td><td>0.662</td><td>0.653</td><td>0.658</td><td>75</td><td>-0.107</td></tr><tr><td>Aparte</td><td>1</td><td>0.571</td><td>0.727</td><td>0.000</td><td>0.000</td><td>0.000</td><td>14</td><td>-0.727</td></tr><tr><td>Delivery</td><td>0.859</td><td>0.831</td><td>0.845</td><td>0.724</td><td>0.826</td><td>0.772</td><td>213</td><td>-0.073</td></tr><tr><td>Entrance</td><td>0.809</td><td>0.727</td><td>0.765</td><td>0.807</td><td>0.586</td><td>0.679</td><td>128</td><td>-0.086</td></tr><tr><td>Exit</td><td>0.833</td><td>0.909</td><td>0.87</td><td>0.762</td><td>0.884</td><td>0.818</td><td>242</td><td>-0.052</td></tr><tr><td>Interaction</td><td>0.791</td><td>0.814</td><td>0.802</td><td>0.741</td><td>0.814</td><td>0.776</td><td>102</td><td>-0.026</td></tr><tr><td>Movement</td><td>0.702</td><td>0.672</td><td>0.687</td><td>0.761</td><td>0.429</td><td>0.548</td><td>119</td><td>-0.139</td></tr><tr><td>Music</td><td>0.969</td><td>0.971</td><td>0.97</td><td>0.931</td><td>0.938</td><td>0.934</td><td>577</td><td>-0.036</td></tr><tr><td>Narration</td><td>0.765</td><td>0.842</td><td>0.802</td><td>0.744</td><td>0.558</td><td>0.638</td><td>120</td><td>-0.164</td></tr><tr><td>Object</td><td>0.826</td><td>0.841</td><td>0.833</td><td>0.725</td><td>0.822</td><td>0.770</td><td>208</td><td>-0.063</td></tr><tr><td>Setting</td><td>0.823</td><td>0.858</td><td>0.84</td><td>0.757</td><td>0.821</td><td>0.788</td><td>190</td><td>-0.052</td></tr><tr><td>Toward</td><td>0.978</td><td>0.984</td><td>0.981</td><td>0.978</td><td>0.978</td><td>0.978</td><td>449</td><td>-0.003</td></tr></tbody></table>

<caption>Table 5: Per-category precision, recall, macro-F1 with *distilcamembert-base*, fine-tuned on 100% and 10% of the data. Column *N* is the number of test items per class, and *Diff* is the 10% macro-F1 minus the 100% one.</caption>

gories *Movement* and *Entrance* are regularly misclassified as *Exit*; a challenge here is that French verb *rentrer* is a contronym, meaning both to go on-stage or offstage. The models have trouble to tease apart *Interaction*, *Aggression*, *Object* and *Movement* from each other; *Aggression* and *Movement* are among those most affected by reduced fine-tuning data. Misclassification of various categories towards *Action* also happens, more so as data for fine-tuning decreases or with classical ML.

# 5 Conclusion and Outlook

The results are encouraging towards automatic large-scale stage-direction classification: 0.7 F1

with a 13-class typology using less than 1,200 manually annotated examples; a more costly set of ca. 5,900 annotations allowed for 0.81 F1. A distilled monolingual model was the best choice, offering satisfactory results and faster fine-tuning than the full model. Besides comparing with large language models and in-context learning, a relevant future task is the detection and classification of *internal* stage directions, implicit from character speech (Galleron, 2018). Reliable multilingual stage direction classification would open the door to large-scale quantitative comparative and diachronic work on an important element of dramatic texts.
## Ethics Statement

The study involved the use of GPUs. Given potential carbon footprint, we assessed the necessity of their use. We consider their use justified given that we observed substantially better results at the task with transfer learning than with classical machine learning models. We compared distilled and full models, ascertaining that the distilled ones perform largely equivalently at the task. We thus propose the use of distilled models for the task described, which will mean less data transfer and GPU usage time in fine-tuning.

The study involves a large corpus of theater in French. A bias in this corpus is the underrepresentation of women authors. Relevant future work to counter this bias would be to encode in TEI and publicly release plays by women authors. Resources such as the database by Bourdic (2022) will facilitate the related bibliographic research.

## Data and Code Availability

The dataframe derived from the FreDraCor TEI documents is at https://doi.org/10.34847/nkl.fde37ug3). Code to run the experiments is at https://doi.org/10.5281/zenodo.10594104, both under open licenses.

## Acknowledgements

We thank the anonymous reviewers for their insightful comments, which helped improve the paper.

This work was supported by IdEx University of Strasbourg, call Attractivité 2020 (MeTHAL project) and Maison Interuniversitaire des Sciences de l'Homme Alsace (MISHA) 2023-24 program (Thealtres project).

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## A Stage direction examples and glosses

For each class, we provide examples from FreDraCor (separated with pipes), followed by their respective English glosses.

<table><thead><tr><td>Class</td><td>Scope</td><td>FreDracor examples and English glosses</td></tr></thead><tbody><tr><td>Action</td><td>General character action category</td><td>Il désigne le garçon de café | Il lit | Elle s'assied<br>He points to the waiter | He reads | She sits down</td></tr><tr><td>Aggression</td><td>Violent action</td><td>Il tire son épée | Il se donne un coup<br>He draws his sword | He strikes himself</td></tr><tr><td>Delivery</td><td>Delivery manner, e.g. regarding voice or vocal expression of emotion</td><td>En riant | À demi-voix<br>Laughing | In a low voice</td></tr><tr><td>Entrance</td><td>Character enters stage</td><td>Ils entrent en scène | Il rentre chez lui<br>They enter the stage | He enters his home</td></tr><tr><td>Exit</td><td>Character exits</td><td>Il sort | Il rentre<br>She exits | He re-enters</td></tr><tr><td>Interaction</td><td>Non-verbal character interaction</td><td>Elle va aussi pour l'embrasser<br>She moves to kiss him | He takes her hand</td></tr><tr><td>Movement</td><td>Character movement (but not exit/entrance)</td><td>Il continue sa marche | Il recule d'un autre côté | Il veut sortir<br>He continues his walk | He retreats to the other side | He wants to exit</td></tr><tr><td>Music</td><td>Tune names (plays with songs); music description</td><td>Air en duo | Musique céleste<br>Duet melody | Celestial music</td></tr><tr><td>Narration</td><td>Long, "narrative quality", for readers</td><td>Cependant VENDE, qui avait été mandée, survient après les acclamations du peuple, elle commande à son Chancelier de déclarer ses intentions à l'Assemblée<br>However, VENDE, who had been summoned, appears after the cheers of the people; she commands her Chancellor to declare her intentions to the Assembly</td></tr><tr><td>Object</td><td>Describes object or interaction with it</td><td>Il lui donne un écu | Elle froisse la lettre<br>He gives her a coin | She crumples the letter</td></tr><tr><td>Setting</td><td>Stage description or play location</td><td>Le théâtre représente un salon | À Sicilie<br>The theater represents a living room | In Sicily</td></tr><tr><td>Toward</td><td>Indicates the addressee of a speech</td><td>À Julie | Au commandeur et au comte<br>Toward Julie | To the commander and the count</td></tr></tbody></table>
## B Mapping between FreDraCor classes and our typology

We list the FreDraCor types that we assigned to each class in our typology. We only worked with single-type labels in FreDraCor, leaving aside stage directions annotated with more than one type (see section 3.1).

For the small number of cases where there was an obvious typo in a FreDraCor label (e.g. *title* spelled as *ttitle*, or *decor* also spelled as *décor*), we accepted both as variants of the same label.

<table><thead><tr><td>Our types</td><td>Corresponding FreDraCor types</td></tr></thead><tbody><tr><td>Entrance</td><td>entrance, entrée</td></tr><tr><td>Exit</td><td>exit, escape</td></tr><tr><td>Setting</td><td>location, decor, décor [sic]</td></tr><tr><td>Narration</td><td>narration, meteo, noise</td></tr><tr><td>Toward</td><td>toward</td></tr><tr><td>Aparte</td><td>aparte, alone</td></tr><tr><td>Delivery</td><td>together, call, interrupt, loud, low, laugh, silence, quiet, cry, shout, nervous, ironic, anger, serious, happy, hesitate, enthousiasm, emotion, emphasis, friendly, grimace, feeling, furious, continue, sing, repeat</td></tr><tr><td>Interaction</td><td>kiss, touch, help, pull, push</td></tr><tr><td>Aggression</td><td>kill, fight, hit, suicide, threat</td></tr><tr><td>Action</td><td>action, watch, show, paint, pray, jump, read, kneel, fall, knock, write, drink, search, open, eat, sleep, stand, sit, move, listen, ring</td></tr><tr><td>Movement</td><td>closer, away, walk, follow, back</td></tr><tr><td>Object</td><td>costume, throw, tear, get, give, dress, drop, close</td></tr><tr><td>Music</td><td>music, title, ttitle [sic], bis</td></tr></tbody></table>