Venue: NAACL 2022

# Combining Humor and Sarcasm for Improving Political Parody Detection

 Xiao Ao\({}^{\alpha}\)  Danae Sanchez Villegas\({}^{\alpha}\)   Daniel Preotiuc-Pietro\({}^{\beta}\)    Nikolaos Aletras\({}^{\alpha}\)

\({}^{\alpha}\) Computer Science Department, University of Sheffield, UK

\({}^{\beta}\) Bloomberg

{xao3,dsanchezvillegsal,n.aletras}@sheffield.ac.uk dpreotiucpie@bloomerg.net

###### Abstract

Parody is a figurative device used for mimicking entities for comedic or critical purposes. Parody is intentionally humorous and often involves sarcasm. This paper explores jointly modelling these figurative tropes with the goal of improving performance of political parody detection in tweets. To this end, we present a _multi-encoder_ model that combines three parallel encoders to enrich parody-specific representations with humor and sarcasm information. Experiments on a publicly available data set of political parody tweets demonstrate that our approach outperforms previous state-of-the-art methods.1

Footnote 1: Code is available here [https://github.com/iamossarl/Multi_Encoder_Model_for_Political_Parody_Prediction](https://github.com/iamossarl/Multi_Encoder_Model_for_Political_Parody_Prediction)

2020

## 1 Introduction

Parody is a figurative device which imitates entities such as politicians and celebrities by copying their particular style or a situation where the entity was involved Rose (1993). It is an intrinsic part of social media as a relatively new cometdic form Vis (2013). A very popular type of parody is political parody, which is used to express political opposition and civic engagement Davis et al. (2018).

One of the hallmarks of parody expression is the deployment of other figurative devices, such as humor and sarcasm, as emphasized on studies of parody in linguistics Haiman et al. (1998); Highfield (2016). For example, in Table 1 the text expresses sarcasm about Myspace2 being a 'winning technology', while mocking the fact that three more popular social media sites were unavailable. This example also highlights the similarities between parody and real tweets, which may pose issues to misinformation classification systems Mu and Aletras (2020).

Footnote 2: [https://myspace.com](https://myspace.com)

These figurative devices have so far been studied in isolation to parody. Previous work on modeling humor in computational linguistics has focused on identifying jokes, i.e., short comedic passages that end with a hilarious line Utezron (1991), based on linguistic features Taylor and Mazlack (2004); Purandare and Litman (2006); Kiddon and Brun (2011) and deep learning techniques Chen and Soo (2018); Weller and Seppi (2019); Annamoradnejad Zoghi (2020). Similarly, computational approaches for modeling sarcasm (i.e., a form of verbal irony used to mock or convey content) in texts have been explored Davidov et al. (2010); Gonzalez-Ibanez et al. (2011); Liebrecht et al. (2013); Rajadesingan et al. (2015); Ghosh et al. (2020), including multi-modal utterances, i.e. texts, images, and videos Cai et al. (2019); Castro et al. (2019); Oprea and Magdy (2020). Recently, parody has been studied with natural language processing (NLP) methods by Maronikolakis et al. (2020) who introduced a data set of political parody accounts. Their method for automatic recognition of posts shared by political parody accounts on Twitter is solely based on vanilla transformer models.

In this paper, we hypothesize that humor and sarcasm information could guide parody specific text encoders towards detecting nuances of figu

\begin{table}
\begin{tabular}{|c|p{170.7pt}|} \hline
**Twitter Handle** & @Queen_UK \\ \hline
**Parody****Rued** & Boris Johnson on the phone. Very smug that \#myspace hasn’t gone down. Says he’s always backed winning technologies \#whatspdown \#instagramdown \#FacebookIsDown \\ \hline \end{tabular}
\end{table}
Table 1: Example of a _parody_ tweet3 by the Twitter handle @Queen_UK. Humor and sarcasm are expressed simultaneously.



rative language. For this purpose, we propose a _multi-encoder_ model (SS2) consisting of three parallel encoders that are subsequently fused for parody classification. The first encoder learns parody specific information subsequently enhanced using the representations learned by a humor and sarcasm encoder respectively.

Our contributions are: (1) new state-of-the-art results on political parody detection in Twitter, consistently improving predictive performance over previous work by Maronikolakis et al. (2020); and (2) insights on the limitations of neural models in capturing various linguistic characteristics of parody from extensive qualitative and quantitative analyses.

## 2 Multi-Encoder Model for Political Parody Prediction

Maronikolakis et al. (2020) define political parody prediction as a binary classification task where a social media post \(T\), consisting of a sequence of tokens \(T=\{t_{1},...,t_{n}\}\), is classified as real or parody. Real posts have been authored by actual politicians (e.g., realDonaldTrump) while parody posts come from their corresponding parody accounts (e.g., realDonaldTrFan).

Parody tends to express complex tangled semantics of both humor and sarcasm simultaneously [9, 3]. To better exploit this characteristic of parody, we propose a _multi-encoder_ model that consists of three parallel encoders, a feature-fusion layer and a parody classification layer depicted in Fig.1.4.

Footnote 4: Early experiments with multi-task learning did not result in improved performance. The results of these experiments can be found in Appendix A.

### Text Encoders

ParodyAs a task-specific parody encoder, we use the vanilla pretrained BERTweet [6], a BERT [5] based model pre-trained on a corpus of English Tweets and fine-tuned on the parody data set (SS3.1).

HumorTo capture humor specific characteristics in social media text, we use the data set introduced by Annamoradnejad and Zoghi (2020) which contains humorous and non-humorous short texts collected from Reddit and Huffington Post. First, we adapt BERTweet using domain-adaptive pre-training [7, 8] on 10,000 randomly selected humor-only short texts with masked language modeling. Subsequently, we use a continual learning strategy [1, 7] to gradually learn humor-specific properties by further finetuning BERTweet on a humor classification task (i.e., predicting whether a text is humorous or not) by using 40,000 randomly selected humorous and non-humorous short texts from the humor corpus described above (see Figure 2).

SarcasmSimilar to humor, we extract sarcasm-related semantic information from a post \(T\) by using sarcasm annotated data sets from Oprea and Magdy (2020) and Rajadesingan et al. (2015). The first data set consists of 777 and 3,707 sarcasm and non-sarcasm posts from Twitter and the second data set consists of 9,104 sarcasm and more than 90,000 non-sarcasm posts from Twitter. We first perform domain-adaptive pre-training of BERTweet on all sarcastic posts with masked language modeling. Then, we fine-tune the model on a sarcasm classification task, similar to the humor encoder (see Figure 3). For the fine-tuning step, we use the 9,881 sarcastic tweets and 10,000 randomly sampled non

Figure 1: The structure of our _multi-encoder_ model for combining humor and sarcasm information for political parody prediction.

Figure 3: _Sarcasm Encoder_.

 

sarcasm tweets from the two data sets (i.e., 3,707 from the first and 6,293 from the second).

We compute parody \(f^{t}\), humor \(f^{h}\), and sarcasm \(f^{s}\) representations by extracting the 'classification' [CLS] token from each encoder respectively, where \(f\in\mathbf{R}^{768}\).

### Combining Encoders

We explore three approaches to combine \(f^{t}\), \(f^{h}\), and \(f^{s}\) representations.

ConcatenationFirst, the three text representations are simply concatenated to form a combined representation \(f\in\mathbf{R}^{768\times 3}\).

Self-AttentionWe also use a 4-head self-attention5 mechanism Vaswani et al. (2017) on \(f^{t},f^{h},f^{s}\). The goal is to find correlations between representations and learn the contribution of each encoder in the final representation.

Footnote 5: Early experimentation with larger attention heads did not improve results in the dev set.

Max-PoolingFinally, we perform a max-pooling operation on each dimension of \(f^{t}\), \(f^{h}\), \(f^{s}\) to obtain a representation \(f\in\mathbf{R}^{768}\). The aim is to use the most dominant features learned by each encoder.

### Classification

Finally, we pass the combined representation \(f\) to a classification layer with a sigmoid activation function for predicting whether a post is a parody or not. Three encoders are fine-tuned simultaneously on the parody data set (SS3.1).6

Footnote 6: Early experimentation with humor and sarcasm encoders frozen during the fine-tuning process did not show any performance improvement.

## 3 Experimental Setup

### Data

We use the data set introduced by Maronikolakis et al. (2020) which contains 131,666 tweets written in English, with 65,956 tweets from political parody accounts and 65,710 tweets posted by real politician accounts. The data set is publicly available7 and allows us to compare our results to state-of-the-art parody detection methods.

Footnote 7: [https://archive.org/details/parody_data_acl20](https://archive.org/details/parody_data_acl20)

We use the three data splits provided: (i) _Person Split_, each split (train, dev, test) contains tweets from different real - _parody_ account pairs; (ii) _Gender Split_, two different splits based on the gender of the politicians (i.e., female accounts in train/dev and male in test, and male accounts in train/dev and female in test); _Location Split_, data is split according to the location of the politicians in three groups (US, UK, Rest of the World or RoW). Each group is assigned to the test set and the other two groups to the train and dev sets.

### Baselines

We compare our _multi-encoder_ models with transformers for parody detection Maronikolakis et al. (2020): **BERT**Devlin et al. (2019) and **RoBERTa**Liu et al. (2019). Also, we compare our models to **BERTwet**Nguyen et al. (2020).

### Implementation details

Humor EncoderFor adaptive pre-training, the batch-size is set to \(16\) and the number of training epochs is set to \(3\) with a learning rate of \(2e^{-5}\). For humor classification, we use batch size of \(128\) and the number of epochs is set to \(2\) with a learning rate of \(3e^{-5}\).

Sarcasm EncoderWe pretrain using a batch-size of \(16\) over \(5\) epochs with a learning rate of \(2e^{-5}\). For fine-tuning on a sarcasm classification task, we use the \(9,881\) sarcasm tweets and \(10,000\) randomly sampled non-sarcasm tweets from the two data sets (i.e., \(3,707\) from the first and \(6,293\) from the second) using the same hyperparameters to the humor-specific encoder.

Multi-encoderFor the complete _multi-encoder_ model, we use a batch size of \(128\) and the learning rate is set to \(2e^{-5}\). The entire model is fine-tuned for \(2\) epochs.

### Evaluation

We evaluate the performance of all models using F1 score as Maronikolakis et al. (2020). Results are obtained over \(3\) runs using different random seeds reporting average and standard deviation.

## 4 Results

### Predictive Performance

Table 2 shows the results for parody detection on the _Person Split_. We observe that _BERTweet_ has the best performance (F1: \(90.72\)) among transformer-based models (_BERT_, _RoBERTa_, _BERTweet_), outperforming previous state-of-the-art by Maronikolakis et al. (2020). This is due to the fact that _BERTweet_ has been specifically pre-trained on



Twitter text. Similar behavior is observed on the _Gender_ and _Location_ splits (see Table 3 and 4 respectively).

Our proposed _multi-encoder_ achieves the best performance when using _Self-Attention_ to combine the three parallel encoders (F1: \(91.19\); \(89.97\), \(88.56\); \(88.37\), \(87.91\), \(87.16\); for _Person_, _Gender_, and _Location_ splits respectively). Moreover, it outperforms the best single-encoder model _BERTweet_ in the majority of cases which corroborates that parody detection benefits from combining general contextual representations with humor and sarcasm specific information, as humor and sarcasm are important characteristics of parody [20, 2]. On the other hand, simply concatenating the three parallel encoders degrades the performance across different splits (_person_: \(88.99\); _Gender_: \(86.84\), \(84.21\)_Location_: \(85.41\), \(84.74\), \(83.62\)). This happens because the concatenation operation treats the three encoders as equally important. While humor and sarcasm are related to parody, they may not necessarily have the same relevance as indicators of parody.

Our best performing model (_Self-Attention_) outperforms the vanilla _BERTweet_ by \(3\) F1 points when trained on female accounts and by almost 2 F1 points when trained on male accounts. We speculate that the additional linguistic information from the two encoders (i.e., sarcasm and humor) is more beneficial in low data settings. The number of female politicians is considerably smaller than males in the data set (see Maronikolakis et al. (2020) for more details).

### Ablation Study

We also examine the effect of combining parody-specific representations with humor and sarcasm information by running an ablation study. We compare performance of four models: using parody representations only (P), and combining parody representations with humor (P+H), or sarcasm (P+S) information, as well as with both (P+S+H). The results of this analysis are depicted in Tables 5, 6 and 7. We observe that both sarcasm and humor contribute to the performance gain, but using both is more beneficial. Modelling sarcasm leads to more gains than humor and this could be attributed to the characteristics of the parody corpus, namely that it focuses primarily on the political domain, which have a high sarcastic component [1].

## 5 Error Analysis

Finally, we perform an error analysis to examine the behavior and limitations of our best-performing model (_multi-encoder_ with Self-Attention).

The next two examples correspond to real tweets that were misclassified as parody:

* _Congratulations, <mention>! <url>_.
* _It's a shame that Boris isn't here answering questions from the public this evening._

We speculate that the model misclassified these tweets as parody because they contain terms that

\begin{table}
\begin{tabular}{l|c|c|c} \hline  & \multicolumn{3}{c}{**Location**} \\ \cline{2-4} Model & \begin{tabular}{c} **UK+US** \\ **RoV** \\ \end{tabular} & \begin{tabular}{c} **RoW+US** \\ \end{tabular} & 
\begin{tabular}{c} **RoW+UK** \\ \end{tabular} \\ \hline \multicolumn{4}{l}{**Single-Encoder**} \\ BERT\({}^{**}\) & \(86.69\pm 0.45\) & \(83.78\pm 0.19\) & \(83.12\pm 0.60\) \\ RoBERTA\({}^{**}\) & \(87.70\pm 0.45\) & \(85.10\pm 0.27\) & \(85.99\pm 0.61\) \\ BERTweet & \(88.21\pm 0.26\) & \(87.85\pm 0.24\) & \(\mathbf{87.18\pm 0.41}\) \\ \hline \multicolumn{4}{l}{**Multi-encoder (Ours)**} \\ Concatenation & \(85.41\pm 0.26\) & \(84.74\pm 0.20\) & \(83.62\pm 0.35\) \\ Self-Attention & \(\mathbf{88.37\pm 0.28}\) & \(\mathbf{87.91\pm 0.19}\) & \(87.16\pm 0.37\) \\ Max-Pooling & \(88.25\pm 0.39\) & \(86.49\pm 0.33\) & \(86.54\pm 0.41\) \\ \hline \end{tabular}
\end{table}
Table 4: F1-scores on the _Location Split_. \({}^{**}\) Results from Maronikolakis et al. (2020). Best results are in bold.

\begin{table}
\begin{tabular}{l|c|c|} \hline \multicolumn{3}{c}{**Person**} \\ \hline \multicolumn{2}{l}{**Model**} & \multicolumn{1}{c}{**F1**} \\ \hline \multicolumn{2}{l}{**Single-Encoder**} \\ \hline BERT\({}^{**}\) & \(87.65\pm 0.18\) & \\ RoBERTta\({}^{**}\) & \(89.66\pm 0.33\) & \\ BERTweet & \(90.72\pm 0.31\) & \\ \hline \multicolumn{2}{l}{**Multi-encoder (Ours)**} \\ \hline Concatenation & \(88.99\pm 0.17\) & \\ Self-Attention & \(\mathbf{91.19\pm 0.31}\) & \\ Max-Pooling & \(91.05\pm 0.30\) & \\ \hline \end{tabular}
\end{table}
Table 2: F1-scores for parody detection on the _Person Split_. \({}^{**}\) Results from Maronikolakis et al. (2020). Best results are in bold.

\begin{table}
\begin{tabular}{l|c|c|c|} \hline \multicolumn{3}{c}{**Gender**} \\ \hline \multicolumn{2}{l}{**Model**} & \multicolumn{1}{c}{**M→F**} & \multicolumn{1}{c}{**F→M**} \\ \hline \multicolumn{2}{l}{**Single-Encoder**} \\ \hline BERT\({}^{**}\) & \(85.85\pm 0.28\) & \(84.40\pm 0.35\) \\ RoBERTta\({}^{**}\) & \(87.11\pm 0.31\) & \(84.87\pm 0.38\) \\ BERTweet & \(88.01\pm 0.29\) & \(85.57\pm 0.27\) \\ \hline \multicolumn{2}{l}{**Multi-encoder (Ours)**} \\ Concatenation & \(86.84\pm 0.15\) & \(84.21\pm 0.22\) \\ Self-Attention & \(\mathbf{89.97\pm 0.34}\) & \(\mathbf{88.56\pm 0.39}\) \\ Max-Pooling & \(88.39\pm 0.27\) & \(86.89\pm 0.56\) \\ \hline \end{tabular}
\end{table}
Table 3: F1-scores on the _Gender Split_. \({}^{**}\) Results from Maronikolakis et al. (2020). Best results are in bold.

 

are related to sarcastic short texts such as user mentions, punctuation marks (_!_), and negation (_isn't_) Gonzalez-Ibanez et al. (2011); Highfield (2016).

The following two examples correspond to parody tweets that were misclassified as real:

* _Hey America, it's time to use your safe word._
* _I fully support the Digital Singles Market._

Example (3) is a call-to-action message, while Example (4) is a statement expressing support for a particular subject. These statements are written in a style that is similar to political slogans or campaign speeches Fowler et al. (2021) that the model fails to recognise. As a result, in addition to humor and sarcasm semantics, the model might be improved by integrating knowledge from the political domain such as from political speeches.

## 6 Conclusion

In this paper, we studied the impact of jointly modelling figurative devices to improve predictive performance of political parody detection in tweets. Our motivation was based on studies in linguistics which emphasize the humorous and sarcastic components of parody Haiman et al. (1998); Highfield (2016). We presented a method that combines parallel encoders to capture parody, humor, and sarcasm specific representations from input sequences, which outperforms previous state-of-the-art proposed by Maronikolakis et al. (2020).

In the future, we plan to combine information from other modalities (e.g., images) for improving parody detection Sanchez Villegas and Aletras (2021); Sanchez Villegas et al. (2021).

## Acknowledgements

We would like to thank all reviewers for their valuable feedback. DSV is supported by the Centre for Doctoral Training in Speech and Language Technologies (SLT) and their Applications funded by the UK Research and Innovation grant EP/S023062/1.

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\begin{table}
\begin{tabular}{|l|c|c|} \hline \multicolumn{3}{|c|}{**Gender**} \\ \hline
**Model** & **M\textgreater{}F** & **F\textgreater{}M** \\ \hline
**Single-Encoder** & & \\ BERTweet (P) & \(88.01\pm 0.29\) & \(85.57\pm 0.27\) \\ \hline
**Multi-encoder (Ours)** & & \\ Concatenation (P+S+H) & \(86.84\pm 0.15\) & \(84.21\pm 0.22\) \\ Concatenation (P+S) & \(86.93\pm 0.40\) & \(83.70\pm 0.41\) \\ Concatenation (P+H) & \(86.58\pm 0.31\) & \(83.34\pm 0.38\) \\ Self-Attention (P+S+H) & \(\mathbf{89.97\pm 0.34}\) & \(\mathbf{88.56\pm 0.39}\) \\ Self-Attention (P+S) & \(89.49\pm 0.37\) & \(88.23\pm 0.44\) \\ Self-Attention (P+H) & \(88.71\pm 0.42\) & \(87.62\pm 0.50\) \\ Max-Pooling (P+S+H) & \(88.39\pm 0.27\) & \(86.89\pm 0.56\) \\ Max-Pooling (P+S) & \(88.36\pm 0.46\) & \(86.55\pm 0.49\) \\ Max-Pooling (P+H) & \(88.14\pm 0.52\) & \(86.53\pm 0.53\) \\ \hline \end{tabular}
\end{table}
Table 6: F1-scores for parody detection on the _Gender Split_ with various settings: parody (P) representations only, and combining parody representations with humor (P+H), or sarcasm (P+S) information, as well as with both (P+S+H). Best results are in bold.

\begin{table}
\begin{tabular}{|l|c|c|c|} \hline \multicolumn{3}{|c|}{**Location**} \\ \hline
**Model** & **UK+US** & **Raw+US** & **Row+UK** \\  **- Box** & **=****Wk** & **=****Wk** \\ \hline
**Single-Encoder** & & & \\ BERTweet (P) & \(88.21\pm 0.26\) & \(87.85\pm 0.24\) & \(\mathbf{87.18\pm 0.41}\) \\ \hline
**Multi-encoder (Ours)** & & & \\ Concatenation (P+S+H) & \(85.41\pm 0.26\) & \(84.74\pm 0.20\) & \(83.62\pm 0.35\) \\ Concatenation (P+S+S) & \(85.92\pm 0.24\) & \(85.67\pm 0.18\) & \(84.09\pm 0.39\) \\ Confacination (P+H) & \(85.39\pm 0.29\) & \(\mathbf{85.33\pm 0.26}\) & \(83.75\pm 0.44\) \\ Self-Attention (P+S) & \(88.43\pm 0.28\) & \(\mathbf{87.91\pm 0.19}\) & \(87.16\pm 0.37\) \\ Self-Attention (P+S) & \(88.24\pm 0.33\) & \(87.88\pm 0.23\) & \(86.47\pm 0.32\) \\ Self-Attention (P+H) & \(88.13\pm 0.35\) & \(87.05\pm 0.28\) & \(85.36\pm 0.40\) \\ Max-Pooling (P+S+H) & \(88.25\pm 0.38\) & \(86.49\pm 0.38\) & \(86.54\pm 0.41\) \\ Max-Pooling (P+S) & \(88.28\pm 0.42\) & \(87.83\pm 0.39\) & \(86.56\pm 0.36\) \\ Max-Pooling (P+H) & \(88.22\pm 0.52\) & \(86.44\pm 0.42\) & \(85.96\pm 0.45\) \\ \hline \end{tabular}
\end{table}
Table 7: F1-scores for parody detection on the _Decation Split_ with various settings: parody (P) representations (P+H), or sarcasm (P+S) information, as well as with both (P+S+H). Best results are in bold.

 

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Multitask-Learning

We also tested applying _multi-task learning_ approaches Caruana (1993) to use either sarcasm prediction (P+S), humor prediction (P+H) or both (P+S+H) as auxiliary tasks for parody detection. We utilize BERTweet as the share encoder and independent classification layers for parody and humor or sarcasm. Three sets of weights are applied to losses from each independent classification layer and the three layers are stacked. The best results are chosen and depicted in Table 8, Table 9 and Table 10.

[TABLE:A2.T10][E

\begin{table}
\begin{tabular}{|l|c|c|c|} \hline \multicolumn{4}{|c|}{**Location**} \\ \hline \multirow{2}{*}{**Model**} & **UK+US** & **RoW+US** & **RoW+UK** \\  & \(\mathbf{\rightarrow}\)**RoW** & \(\mathbf{\rightarrow}\)**UK** & \(\mathbf{\rightarrow}\)**US** \\ \hline
**Single-Encoder** & & & \\ BERTweet (P) & \(\mathbf{88.21\pm 0.26}\) & \(\mathbf{87.85\pm 0.24}\) & \(\mathbf{87.18\pm 0.41}\) \\ \hline
**Multi-Task** & & & \\ P+S+H & \(86.41\pm 0.17\) & \(86.23\pm 0.20\) & \(85.13\pm 0.29\) \\ P+S & \(87.74\pm 0.36\) & \(87.26\pm 0.34\) & \(86.67\pm 0.43\) \\ P+H & \(85.54\pm 0.38\) & \(84.78\pm 0.47\) & \(84.15\pm 0.56\) \\ \hline \end{tabular}
\end{table}
Table 10: F1-scores on the _Location Split_ using Multi-task Learning models (P: Parody, S: Sarcasm, H: Humanor). Best results are in bold.

\begin{table}
\begin{tabular}{|l|c|} \hline \multicolumn{2}{|c|}{**Person**} \\ \hline \multicolumn{2}{|c|}{**Model**} & **F1** \\ \hline \multicolumn{2}{|c|}{**Single-Encoder**} \\ \hline \multicolumn{2}{|c|}{BERTweet (P)} & \(\mathbf{90.72\pm 0.31}\) \\ \hline \multicolumn{2}{|c|}{**Multi-Task**} \\ \hline \multicolumn{2}{|c|}{P+SH} & \(87.46\pm 0.18\) \\ \multicolumn{2}{|c|}{P+S} & \(89.41\pm 0.31\) \\ \multicolumn{2}{|c|}{P+H} & \(87.41\pm 0.38\) \\ \hline \end{tabular}
\end{table}
Table 8: F1-scores for parody detection on the _Person Split_ using Multi-task Learning models (P: Parody, S: Sarcasm, H: Humanor). Best results are in bold.

\begin{table}
\begin{tabular}{|l|c|c|} \hline \multicolumn{2}{|c|}{**Gender**} \\ \hline \multicolumn{2}{|c|}{**Model**} & **M→F** & **F→M** \\ \hline \multicolumn{2}{|c|}{**Single-Encoder**} \\ \hline \multicolumn{2}{|c|}{BERtweet (P)} & \(88.01\pm 0.29\) & \(85.57\pm 0.27\) \\ \hline \multicolumn{2}{|c|}{**Multi-Task**} \\ \hline \multicolumn{2}{|c|}{P+S+H} & \(85.28\pm 0.29\) & \(84.10\pm 0.37\) \\ \multicolumn{2}{|c|}{P+S} & \(\mathbf{88.13\pm 0.21}\) & \(\mathbf{86.07\pm 0.44}\) \\ \multicolumn{2}{|c|}{P+H} & \(84.53\pm 0.31\) & \(86.07\pm 0.47\) \\ \hline \end{tabular}
\end{table}
Table 9: F1-scores on the _Gender Split_ using Multi-task Learning models (P: Parodý, S: Sarcasm, H: Humanor). Best results are in bold.