# This joke is [MASK]: Recognizing Humor and Offense with Prompting

Junze Li† Mengjie Zhao‡ Yubo Xie† Antonis Maronikolakis‡ Pearl Pu† Hinrich
Schütze‡
†EPFL ‡LMU Munich
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Humor is a magnetic component in everyday human interactions and
communications. Computationally modeling humor enables NLP systems to
entertain and engage with users. We investigate the effectiveness of
prompting, a new transfer learning paradigm for NLP, for humor recognition. We
show that prompting performs similarly to finetuning when numerous annotations
are available, but gives stellar performance in low-resource humor
recognition. The relationship between humor and offense is also inspected by
applying influence functions to prompting; we show that models could rely on
offense to determine humor during transfer. Disclaimer: This paper contains
model outputs that are offensive by nature.

##  1 Introduction

Humor is one of the most attractive phenomena in human communication,
providing entertainment and relieving mental stress (Mihalcea & Strapparava,
2005; Lefcourt & Martin, 2012). Humor regulates human communication, as a
result, computationally modeling it is expected to improve human-computer
interaction experience (Nijholt et al., 2003; Rayz, 2017).

The first attempt to define humor was in ancient Greece, where ideologists and
philosophers considered human laughter during comedies as a form of scorn
(Morreall, 2020). Superiority theory of humor considers that the laughter is a
type of superiority over other peoples’ _physical defects or shortcomings_.
Incongruity theory focuses on language semantics, claiming that incongruous
meanings in the same context lead to a humor effect (Kant, 1790; Schopenhauer,
1883). This type of humor with the form of “setup + punchline” is widely
studied in NLP; numerous linguistic resources were created (Mihalcea et al.,
2010; Bertero & Fung, 2016; Xie et al., 2020).

Humor recognition (Mihalcea & Strapparava, 2005), striving to identify
humorous texts, is the first step to enable NLP models to understand humor.
_State-of-the-art models_ (Meaney et al., 2021) _rely on transfer learning
from large-scale pretrained language models (PLMs)_ ; finetuning (Devlin et
al., 2019) is a common practice. In this work, we further investigate the
effectiveness of prompting (Brown et al., 2020; Schick & Schütze, 2020; Liu et
al., 2021a) in humor recognition. This is inspired by the fact that recognize
humor of the form “setup + punchline” well matches prompting patterns. E.g.,
we can query a language model to fill in the blank in a pattern “setup +
punchline. It is [MASK]”, by answering “funny” or “normal”. We show that
prompting performs comparably to finetuning when numerous annotations are
available, but it clearly outperforms finetuning in low-resource transfer
scenarios.

Humor is subjective: judgment of what is humorous varies across human
personalities, cultural backgrounds, and commonsense knowledge (Chen & Soo,
2018). As in the superiority theory, it is likely that some humor recognition
datasets include “humorous” content that is offensive, e.g., offensive to
women. This is undesirable because a machine-learning based NLP system, e.g.,
a virtual assistant, should never respond to a user query such as “Tell me a
joke!” with text that is offensive (even if some may view the text as
humorous). It is thus crucial to identify, mitigate, and reduce offense when
modeling humor computationally.

The SemEval-2021 Shared Task shows that the relationship between humor and
offense is subtle and challenging to detect (Meaney et al., 2021). In this
work, we leverage influence functions (If; Cook & Weisberg (1982); Koh & Liang
(2017)) to identify offense when building humor recognition systems based on
transfer learning. For each humorous test example, we locate its influential
training examples and then inspect if they were offensive. Since utilizing If
does not incur architectural modifications (Koh & Liang, 2017), we integrate
it to both finetuning and prompting. If identifies humorous and offensive
training examples, in both finetuning and prompting. However, the accuracy of
identification varies when different resource limitations apply.

We make the following contributions: (i) We investigate the effectiveness of
prompting, the new transfer learning paradigm in NLP, for humor recognition.
Prompting performs similarly to finetuning when enough data is available, but
gives stellar performance in low-resource scenarios. (ii) We integrate
influence functions to finetuning and prompting, and show that the identified
influential training examples indeed contain offensive contents. This
characterization implies that PLM-based humor recognition systems rely on
offensive contents to determine the presence of humor, which is an undesirable
behavior and should be avoided in computational humor.

##  2 Method

We start with introducing finetuning and prompting for humor recognition.
Next, we introduce procedures of estimating the influence of individual
training examples for a test example.

Finetuning. We follow the standard practice of finetuning BERT as Devlin et
al. (2019). We randomly initialize a linear classifier layer and then stack it
on top of BERT. All the parameters are then finetuned using the humor
recognition dataset. Vector of [CLS] is used to represent the sentence.

Figure 1: The prompting method for humor recognition.

Prompting is a recent method of utilizing the PLMs (Brown et al., 2020; Schick
& Schütze, 2020; Liu et al., 2021a). Unlike finetuning that randomly
initializes a new classifier head, prompting reuses the masked-language model
(MLM) head of BERT, requiring no extra new parameters. Prompting reformulates
a sentence $\mathbf{x}$ using a pattern $f_{prompt}(\cdot)$ which contains the
[MASK] token. After that, the PLM is asked to fill in the [MASK] token by
selecting a token from $\mathcal{V}$={$y_{1}$, $y_{2}$}, using the MLM head.
In this work, we use $f_{prompt}(\mathbf{x})=\mathbf{x}\ \ \underline{That}\ \
\underline{is}\ \ \texttt{[MASK]}\ \underline{.}$ and $\mathcal{V}$ =
{“funny”, “normal”}. Figure 1 demonstrates prompting with a concrete example.
To compute the model’s performance, we use mapping “funny” → 1; “normal” → 0
such that standard metrics like accuracy can be computed.

Influence Functions. Following Han & Tsvetkov (2020), we leverage influence
functions (Cook & Weisberg, 1982; Koh & Liang, 2017) to identify the most
influential training examples. For each test example, we compute an influence
score of every training example. Denoting an annotated training example as
$\mathbf{z}_{i}=\left(\mathbf{x}_{i},y_{i}\right)$, the optimal parameters
obtained on the annotated data
$\\{\mathbf{z}_{1},\mathbf{z}_{2},...,\mathbf{z}_{n}\\}$ as
$\hat{\theta}\stackrel{{\scriptstyle\text{ def
}}}{{=}}\arg\min_{\theta\in\Theta}\frac{1}{n}\sum_{i=1}^{n}L\left(\mathbf{z}_{i},\theta\right)$,
where $\theta$ refers to model parameters, $L\left(\mathbf{z},\theta\right)$
refers to the loss, and $n$ refers to the number of examples. An intuitive way
of computing the influence of an example $\mathbf{z}_{i}$ is to inspect how
$\hat{\theta}$ changes, when excluding or including $\mathbf{z}_{i}$ into the
training dataset; i.e., $\mathcal{I}(\mathbf{z}_{i})$ =
$\hat{\theta}_{-\mathbf{z}_{i}}-\hat{\theta}$, where
$\hat{\theta}_{-\mathbf{z}_{i}}\stackrel{{\scriptstyle\text{ def
}}}{{=}}\arg\min_{\theta\in\Theta}\frac{1}{n-1}\sum_{z\neq\mathbf{z}_{i}}L\left(\mathbf{z}_{i},\theta\right)$.
But this is computationally expensive because it requires retraining the model
for every example $\mathbf{z}_{i}$. Cook & Weisberg (1982) propose influence
functions as an efficient approximation by considering up-weighting a training
example $\mathbf{z}_{i}$ by a small value $\epsilon_{i}$. Because we are more
interested in the change of model loss/predictions, we compute the influence
of a training example $\mathbf{z}_{i}$ _to the prediction of a test example_
$\mathbf{z}_{\text{test}}$, following Koh & Liang (2017):

$\scriptsize\begin{split}\mathcal{I}(\mathbf{z}_{i},\mathbf{z}_{\text{test
}})&\stackrel{{\scriptstyle\text{def}}}{{=}}\frac{dL(\mathbf{z}_{\text{test
}},\hat{\theta})}{d\epsilon_{i}}\\\
&=-\nabla_{\theta}L(\mathbf{z}_{\text{test}},\hat{\theta})^{\top}(\frac{1}{n}\sum_{j=1}^{n}\nabla_{\theta}^{2}L(\mathbf{z}_{j},\hat{\theta}))^{-1}\\!\nabla_{\theta}L(\mathbf{z}_{i},\hat{\theta}).\end{split}$

For each test example $\mathbf{z}_{\text{test}}$, we compute an influence
score of each training example and then conduct $z$-normalization. We can then
inspect whether or not the most influential training examples contain offense
and then correct or discard them.

##  3 Datasets

We leverage the human-annotated HaHackathon Dataset (HHD) of SemEval 2021 Task
7 (Meaney et al., 2021). An HHD example consists of a text and two
annotations: A binary humor label and an offense score ranging from 1 to 5. A
humorous and not offensive example is “Why do birds fly south in the Winter?
Because its too far to walk! ” while a humorous but offensive example is
“Getting a girlfriend is a lot like getting a car. The more money you have,
the more options you have.”.

HHD contains humor texts covering eight categories including Sexism, Body,
Origin etc. In this work, we limit our scope to gender-related humor, and
extract related examples with surface form matching using the keywords in
category “Sexism”. We call our constructed dataset Gender Humor Dataset (GHD).
We enforce two constraints when creating GHD: (i) For gender-related texts and
the ones not related to gender, we keep the same amount of humorous and non-
humorous texts. This makes sure that a model cannot make predictions through
simple heuristics. (ii) We use 80% examples for training and 20% examples for
testing. Appendix Figure 3 plots the offense score distribution of GHD
training examples. We observe a long-tail distribution, where the maximum
offense score is 4.7 and the average offense score is 0.64.

##  4 Experiment

We conduct a series of experiments to answer the following research questions.
RQ1: How well does prompting perform in humor recognition, compared to
finetuning when the amount of labeled data varies? RQ2: Do the influential
training examples correspond to humorous test examples contain offensive
contents? RQ3: What is the difference between the identified offensive
training examples in finetuning and prompting?

In our experiments, we use BERT-base-uncased (Devlin et al., 2019) as our PLM.
We implement our models using PyTorch (Paszke et al., 2019) and HuggingFace
(Wolf et al., 2020). Detailed experiment configurations are in Appendix §A.2.

###  4.1 Comparing Finetuning and Prompting Humor Recognition Methods (RQ1)

We firstly compare the performance of finetuning and prompting of recognizing
humor with HHD. We conduct experiments using all of the training examples
(“Full data”) as well as randomly sampled few-shot data. For few-shot
experiments, we repeat each experiment five times and report mean and standard
deviation. Figure 2 left shows the results. It can be observed that prompting
has an overall better performance than finetuning in the few-shot scenarios;
the performance difference between these two methods decreases as more
examples become available. Lastly, prompting and finetuning perform similarly
when all HHD training examples are used.

| Prompting | Finetuning
---|---|---
| F1-score | Acc. | F1-score | Acc.
16-shot |  74.20 (10.65) |  67.86 (5.57) | 67.32 (15.03) | 58.38 (4.07)
32-shot |  79.68 (01.15) |  70.90 (0.96) |  75.66 (00.73) | 62.28 (1.37)
64-shot |  80.36 (00.52) | 73.21 (0.46) |  80.29 (02.25) |  74.64 (3.66)
128-shot |  83.26 (00.83) |  78.08 (0.84) |  81.07 (02.88) | 74.48 (4.32)
Full data | 92.85 | 91.00 | 93.00 | 91.30

Figure 2: Left: Humor recognition results (%) of prompting and finetuning on
HHD. We report mean and standard deviation (in parentheses) of task
performance computed with five runs. Right: offense score distribution of 30
most influential training samples.

###  4.2 Offensive Influential Examples (RQ 2-3)

In this section, we investigate if influence functions (If) can be used to
identify training examples that are sexism-offensive. For a test example, we
compute influence score of every training example and then inspect _the most
influential ones_ to see if strong offensive contents present. Similar to
section §4.1, we compare the results of prompting and finetuning, in full data
and few-shot scenarios, but using GHD. We adopt the same experiment
configurations as §4.1.

####  4.2.1 Model Trained on Full Dataset

Following Han et al. (2020), we randomly sample 50 test examples and identify
their top 10, 30, and 50 most influential training examples. We then report
the averaged offensive score. In addition, we use the average offensive score
of all the GHD training examples as a reference.

| TopK Influential | Avg.Offen.(pos) | Avg.Offen.(neg)
---|---|---|---
Full Data | FT Top10 |  1.39 ($\pm$0.96) |  0.45 ($\pm$0.61)
FT Top30 |  1.23 ($\pm$0.93) |  0.53 ($\pm$0.64)
FT Top50 |  1.16 ($\pm$0.89) |  0.61 ($\pm$0.69)
PT Top10 |  1.02 ($\pm$0.93) |  0.22 ($\pm$0.31)
PT Top30 |  0.95 ($\pm$0.82) |  0.24 ($\pm$0.30)
PT Top50 |  0.94 ($\pm$0.85) |  0.30 ($\pm$0.31)
Reference | 0.64
128-Shot | FT Top8 |  1.24 ($\pm$1.24) |  0.84 ($\pm$1.04)
FT Top16 |  1.33 ($\pm$1.27) |  0.93 ($\pm$1.14)
FT Top32 |  1.33 ($\pm$1.27) |  0.93 ($\pm$1.14)
PT Top8 |  1.53 ($\pm$1.26) |  0.59 ($\pm$0.67)
PT Top16 |  1.49 ($\pm$1.25) |  0.64 ($\pm$0.77)
PT Top32 |  1.30 ($\pm$1.21) |  0.67 ($\pm$0.76)
Reference | 0.67
Table 1:  Averaged offense score of identified TopK influential training
examples. As an example, the top-3 ranked joke “In many U.S. States offenders
receive a harsher penalty for hitting a dog than they do for hitting a woman.
That’s outrageous either way you’re slapping a bxxxh.” is obviously offensive,
with a score of 3.2. “FT”: finetuning; “PT”: prompting. Different amount of
training examples are used: Full data and 128 training examples; “Reference”
shows the averaged offensive score of training examples. “pos”: humorous test
examples; “neg”: non-humorous test examples.

Table 1, “Full Data” rows, shows that the most influential training examples
for humorous test examples (“pos”) indeed have high offensive score. For
example, the average offense of the top ten influential training examples is
1.39 for finetuning and 1.02 for prompting, which are clearly higher than the
reference 0.64 of all the training examples. On the other hand, for non-
humorous test examples (“neg”), the identified influential training examples
have offense scores smaller than reference: 0.45 for finetuning and 0.22 for
prompting.

In Figure 2 right, we show the offense score distribution of the top 30 most
influential training examples for humorous (“pos”) and non-humorous (“neg”)
test examples. For non-humorous examples, most of the influential training
examples have low offense scores, ranging from 0 to 1. However for humorous
examples, the offense scores spread out to different values, achieving a
maximum of 3. Overall, these observations imply that _offensive content
contributes to the presence of humor_ , which clearly is an undesirable
property given the fact that there are non-offensive humorous training
examples.

Similar to Figure 2 left, finetuning and prompting perform similarly on
identifying the influential training examples in this full data scenario.

####  4.2.2 Model Trained on Few-shot Data

We also investigate the results of identifying influential training examples
in low resource scenarios. Table 1 also shows results when using 128 training
examples. We repeat each experiment three times and then report the average.

In the 128-shot scenario, we observe that both finetuning and prompting
perform worse in identifying correct influential training examples than in the
full data scenario. This is more obvious for finetuning: For non-humorous
(“neg”) test examples, the identified most influential training examples have
high offensive scores (e.g, 0.84 of the top eight examples), surpassing the
reference (0.67), similar to the influential examples to the humorous (“pos”)
test examples. This is undesirable because the model relies on incorrect
training examples for non-humorous test examples. This reflects that
finetuning, which often requires numerous annotated data to perform well,
cannot collaborate with influence functions to accurately identify important
training examples in this low-resource scenario.

In contrast, combining prompting and influence functions gives more accurate
identification of influential and offensive training examples: The most
influential examples for humorous texts have offensive scores higher than the
reference and the most influential examples for non-humorous texts have
offensive scores lower than the reference. Overall, _prompting should be
preferred in recognizing humor and identifying influential offensive training
examples when the annotated data is limited._

##  5 Related Work

Early work on humor recognition is based on human-designed stylistic features,
e.g., alliteration chain, semantic ambiguity, and semantic relatedness. They
have been shown to be very effective (Mihalcea & Strapparava, 2005; Attardo,
2010). More recent works focus on deep neural network models like CNN (Morales
& Zhai, 2017; Liu et al., 2018; Chen & Lee, 2017; Chen & Soo, 2018; Weller &
Seppi, 2019). State-of-the-art systems rely on transfer learning with PLMs
(Fan et al., 2020; Xie et al., 2021). E.g., DeepBlue (Song et al., 2021)
ensembles several finetuned RoBERTa (Liu et al., 2019) and ALBERT (Lan et al.,
2020) models, achieving high performance of humor recognition in SemEval
(Meaney et al., 2021). In this work, we explore the effectiveness of prompting
(Brown et al., 2020; Schick & Schütze, 2020; Liu et al., 2021a; Li & Liang,
2021; Lester et al., 2021; Zhao & Schütze, 2021; Liu et al., 2021b; Zhao et
al., 2022), a new transfer learning paradigm of utilizing PLMs, for humor
recognition. We show that prompting performs better than (resp. comparably to)
finetuning in low-resource (resp. high-resource) scenarios.

Identifying offense in computational humor is of high importance because we
want our NLP systems to entertain users with no harm. It is non-trivial to
detect the subtle relationship between humor and offense (Meaney et al., 2021;
Hofmann et al., 2020; Ruch, 2010); we show that influence functions can be
leveraged to identify the offensive examples and is more compatible with
prompting than finetuning in low-resource transfer learning scenarios.

##  6 Conclusion

This paper focuses on humor recognition systems built upon PLMs and transfer
learning. We investigate the effectiveness of prompting for humor recognition
and show that it outperforms finetuning when annotations are limited. By
employing influence functions, we characterize and show that models can rely
on offense to recognize humor during transfer – which is highly undesirable
for real-world applications. Future work may explore methods of decreasing the
offense, and investigate offense related to other aspects than gender/sexism
in humor recognition systems.

## Acknowledgments

This work was partially funded by the European Research Council (grant
#740516) and the German Federal Ministry of Education and Research (BMBF,
grant #01IS18036A).

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##  Appendix A Appendix

###  A.1 Dataset Statistics

In HHD training dataset, there are 4,932 humorous examples and 3,068 non-
humorous examples. In HHD test dataset, there are 615 humorous examples and
385 non-humorous examples. For GHD dataset, we keep the same proportion of
humorous examples and non-humorous examples. In GHD training dataset, there
are 766 humorous examples and 763 non-humorous examples. In GHD test dataset,
there are 190 humorous examples and 193 non-humorous examples. We also show
the offense score distribution of the examples in GHD dataset in Figure 3.

Figure 3: The offense score distribution of Gender Humor dataset

###  A.2 Experiment Configurations

When we compute the influence function results for finetuning and prompting,
we use the same experiment settings. We implement the influence function by
adapting the implementation of Han et al. (2020) using the LiSSA estimation
algorithm (Agarwal et al., 2017). Since deep neural network models like BERT
are not convex, an additional damping term should be added to make sure that
the Hessian matrix is invertible and positive-definite. We choose the damping
term of $3\times 10^{-3}$ as Han & Tsvetkov (2020). The training batch size is
16, the test batch size is 8, the learning rate is $5\times 10^{-5}$, the
number of epochs is 3. To simplify the computation of the influence function,
we do not update the parameters in the embedding layer and the first 8
Transformer layers, and only finetune the parameters in remaining layers as
Han et al. (2020). We load the parameters from the pretrained BERT-base-
uncased model and keep the max input text length of 128. All experiments are
conducted on a machine with Intel Core i7-6700K CPU, Nvidia GeForce GTX 1080
GPU, and 16GB RAM.