Claudio Andrea Manzari

# Explaining the Cabibbo Angle Anomaly

###### Abstract

The Cabibbo-Cobayashi-Maskawa (CKM) matrix parametrizes the misalignement
between the up- and down-quark mass basis in the Standard Model (SM). The
observation of first row CKM unitarity violation has recently emerged as a new
anomaly of the SM, known as the "Cabibbo Angle Anomaly" (CAA). With current
measurements, comparing the elements $V_{ud}$ and $V_{us}$ extracted from beta
and kaon decays respectively, the tension with the SM prediction amounts to
$\sim$$3\,\sigma$. Recently, it has been pointed out that this anomaly can
also be seen as a discrepancy in the determination of the Fermi constant from
muon decay vs $\beta$ and K decays, once CKM unitarity is assumed. In fact,
possible explanations in terms on New Physics fall under two broad classes:
contributions to $\beta$ decay and/or to $\mu$ decay. In this proceedings, we
discuss these solutions in terms of gauge invariant dimension 6 operators in
SMEFT and simplified extensions of the Standard Model. The latter could
introduce correlations with other anomalies in the SM, pointing to new and
interesting directions for model building.

##  1 Introduction

The observed deficit in first row CKM unitarity, known as the Cabibbo Angle
Anomaly (CAA) [1, 2, 3, 4, 5, 6], is among the most intriguing deviation from
the Standard Model (SM) predictions. With the results in Refs. [7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17] and the compilation of Ref. [18], the tension
amounts to $\sim$$3\sigma$,

$\displaystyle|V_{ud}|^{2}+|V_{us}|^{2}+|V_{ub}|^{2}=0.9985(5)\,.$ (1)

Here, $V_{ud}$ is extracted from super-allowed $\beta$ decays
($V_{ud}^{\beta}$), $V_{us}$ from $K\to\mu\nu/\pi\to\mu\nu$ and semi-leptonic
Kaon decays ($K_{\ell 3}$) and $V_{ub}$, from B meson decays. For this
anomaly, $V_{ub}$ is negligible and $|V_{ud}|^{2}/|V_{us}|^{2}\approx 20$,
such that the sensitivity to NP in the determination of $V_{ud}$ is enhanced
with respect to $V_{us}$. In addition, a violation of first column CKM
unitarity has also been observed, where again this element dominates but
$V_{us}$ is replaced by $V_{cd}$, further strenghtening the idea of NP related
to $V_{ud}$. Hence, the presence of New Physics (NP) in the extraction of
$V_{ud}$ is preferred to solve the CAA.
There are two possible and not necessarly distinct solutions: NP contributions
in beta decay and/or in muon decay [19]. The latter is possible since the
Fermi constant, $G_{F}$, is extracted from muon decay and the product
$G_{F}*V_{ud}$ is measured in beta decays. Within an effective theory
approach, these two solutions can be realized by means of operators falling
into four classes, as shown in Fig. 1:

Figure 1: Possible classes of solutions to the CAA within an effective theory
approach.

  1. 1.

four-fermion operators in $\mu\to e\nu\nu$,

  2. 2.

four-fermion operators in $u\to de\nu$,

  3. 3.

modified $W$–$u$–$d$ couplings,

  4. 4.

modified $W$–$\ell$–$\nu$ couplings,

In these proceedings, I discuss the NP effect needed to solve the CAA, first
in terms of SMEFT operators at the dimension 6 level and then of simplified SM
extensions. Interestingly, the latters introduce correlations with other
flavour anomalies, such as $\tau\to\mu\nu\nu$ and semileptonic B decays. This
allows for simultaneous explanations of several tensions and the construction
of more appealing SM extensions, highlighting the need for a complete analysis
of the CAA.

##  2 SMEFT analysis

As discussed before, to solve the CAA, NP can affect muon and/or beta decay
(an additional effect in semi-leptonic $K$ decays would alleviate the tension
even further). An extensive analysis of all the gauge-invariant dimension-$6$
operators affecting $\beta$ and $\mu$ decay is performed in Ref. [19]. Here we
report the main results, using the conventions of Ref. [20].

###  2.1 Four-fermion operators in $\boldsymbol{\mu\to e\nu\nu}$

Taking into account the constraints from the Michel parameter, muonium-
antimuonium oscillations, lepton radiative decays and 3-body lepton flavour
violating decays, the only viable way to modify the extraction of $G_{F}$
proceeds via the SM operator
$Q_{\ell\ell}^{2112}=\big{(}\bar{\ell}_{2}\gamma^{\mu}\ell_{1}\big{)}\big{(}\bar{\ell}_{1}\gamma_{\mu}\ell_{2}\big{)}$
(four-quark operators can only contribute via loop effects and are anyway
discarded). Because of hermiticity, its Wilson coefficient is real and can
interfere constructively or destructively with the SM in muon decay [21, 22,
23, 24, 25]. In order to bring the tension in Eq. 1 at $1\sigma$ we need
$C_{\ell\ell}^{2112}\approx-(8\;\rm TeV)^{-2}$. This Wilson coefficient is
constrained by LEP searches for $e^{+}e^{-}\to\mu^{+}\mu^{-}$ [26]. The bounds
are a order of magnitude weaker than the value preferred by the CAA, but
within reach of future $e^{+}e^{-}$ colliders. However, the impact on the
electroweak (EW) precision observables has to be taken into account to
properly asses the impact of this solution.

###  2.2 Four-fermion operators in $\boldsymbol{d\to ue\nu}$

Here, to have a large enough effect, we need interference with the SM. Taking
into account the stringent constraints from $\pi\to\mu\nu/\pi\to e\nu$ [27,
18], the scalar operators are ruled out and we are left with $Q_{\ell
q}^{\left(3\right)1111}=\big{(}\bar{\ell}_{1}\gamma^{\mu}\ell_{1}\big{)}\big{(}\bar{q}_{1}\gamma_{\mu}q_{1}\big{)}$.
The CAA prefers $C_{\ell q}^{\left(3\right)1111}\approx(10\;\rm TeV)^{-2}$.
Via $SU(2)_{L}$ invariance, this operator generates effects in neutral-current
interactions and at constructive interference with the $\bar{u}u(\bar{d}d)\to
e^{+}e^{-}$ amplitudes at high energies. The limits extracted from non-
resonant di-lepton searches by ATLAS [28] are still non-onstraining but start
to be important in the case of a triplet operator. Interesting correlations
with the latest CMS observations in non-resonant di-lepton searches have been
discussed in Ref. [29].

###  2.3 Modified $\boldsymbol{W}$–$\boldsymbol{u}$–$\boldsymbol{d}$ couplings

There are only two operators modifying the $W$ couplings to quarks $Q_{\phi
q}^{\left(3\right)ij}={\phi^{\dagger}}i\overset{\leftrightarrow}{D}^{I}_{\mu}\phi{{\bar{q}}_{i}}{\gamma^{\mu}}{\tau^{I}}{q_{j}}$
and $Q_{\phi
ud}^{ij}={\tilde{\phi}^{\dagger}}iD_{\mu}\phi{{\bar{u}}_{i}}{\gamma^{\mu}}{d_{j}}$.
First of all, $Q_{\phi ud}^{11(12)}$ generates right-handed $W$–quark
couplings [30], which can solve the CAA, accounting also for the difference
between $K_{\ell 2}$ and $K_{\ell 3}$ decays [2]. A solution with $Q_{\phi
q}^{\left(3\right)11}$ is possible, modifying the left-handed $W$–quark
couplings and data prefer $C_{\phi q}^{\left(3\right)11}\approx-(9\;\rm
TeV)^{-2}$. Due to $SU(2)_{L}$ invariance, constraints from
$D^{0}-\bar{D}^{0}$ and $K^{0}-\bar{K}^{0}$ mixing and from $Z$ decays have to
be taken into account. However, the former bounds can be avoided by a $U(2)$
flavor symmetry and the latter by simultaneous contributions to $Q_{\phi
q}^{\left(1\right)ij}$. For a detailed analysis see Ref. [31].

###  2.4 Modified $\boldsymbol{W}$–$\boldsymbol{\ell}$–$\boldsymbol{\nu}$
couplings

Only the operator
$Q_{\phi\ell}^{(3)ij}={\phi^{\dagger}}i\overset{\leftrightarrow}{D}^{I}_{\mu}\phi{{\bar{\ell}}_{i}}{\gamma^{\mu}}{\tau^{I}}{\ell_{j}}$
generates modified $W$–$\ell$–$\nu$ couplings at tree level. The off-diagonal
Wilson coefficients are neglected to avoid the stringent bounds from charged
lepton flavor violation and since they generate negligible effects. Modified
$W$ couplings to electrons affect muon and beta decay in the same way and
leave the CAA unaffected. On the other hand, $C_{\phi\ell}^{(3)22}$ only
enters in muon decay and provides a viable solution to the anomaly, which
prefers $C_{\phi\ell}^{(3)22}>0$. A non-zero $C_{\phi\ell}^{(3)11}<0$, with
$|C_{\phi\ell}^{(3)22}|<|C_{\phi\ell}^{(3)11}|$, is also required by lepton
flavor universality tests such as $\pi,\,K$ and $\tau$ decays [3, 5]. Also in
this case, a global fit including the impact on the EW precision observables
is required.

##  3 Simplified Models

An exhaustive analysis of simplified models which can generate the operators
discussed in Sec. 2 is beyond the scope of this proceedings. Here, we report
the cases studied in the literature with the focus on emerging correlations
with other anomalies. The possible SM extensions can be sum up by their
effects as follows:

NP in $\boldsymbol{\mu}$ decay

  1. 1.

Singly Charged Scalar Singlet

  2. 2.

Vector Boson Singlet

  3. 3.

Vector Boson Triplet

  4. 4.

Vector-like Leptons

NP in $\boldsymbol{\beta}$ decay

  1. 1.

Vector Boson Triplet

  2. 2.

Vector-like Quarks

  3. 3.

Vector-like Leptons

  4. 4.

Leptoquarks

A neutral vector boson $SU(2)_{L}$ singlet can contribute to the muon decay
amplitude. Taking into account the constrains from charged lepton flavour
violating decays, EW precision observables and LEP bounds, only LFV couplings
can solve the CAA [32].
On the other hand, a vector boson triplet allows a very simple solution of the
CAA via the $W^{\prime}$ contribution to $\beta$ and $\mu$ decays
simultaneously. In addition, the neutral component of the triplet could
introduce interesting correlations with $b\to s\ell\ell$ observables. In fact,
Ref. [33] shows that this model can provide a solution to the CAA and strongly
improve the agreement with data in semileptonic B decays.
Solutions to the anomaly with vector-like quarks (VLQ) and vector-like leptons
(VLL) are discussed in Ref. [1, 2, 34, 35, 36, 31]. They proceed via a
modification of the gauge boson couplings with quarks and leptons
respectively, therefore a global fit to a large set of observables has to be
performed. With VLQs two solutions are possible [34, 6]: one involving two
singlets coupling to up and down quarks respectively and one involving a
doublet coupling to them simultaneously. Interestingly, Ref. [35] finds a VLL
model, made of a singlet coupling with electrons and a triplet coupling with
muons, which solve the CAA and significantly improves the global fit to data.
Leptoquark solutions have been discussed in Refs. [37, 38], where it is shown
that the scalar triplet can address the anomaly once an additional mechanism
to compensate the effect in $D^{0}-\bar{D^{0}}$ is introduced. Eventually,
there is the Singly Charged Scalar Singlet, which is a $SU(2)_{L}\times
SU(3)_{C}$ singlet with hypercharge $+1$. Because of its quantum numbers, it
cannot couple to quarks and naturally introduces lepton flavour violation,
contributing to muon decay amplitude and providing a solution to the CAA [39].
In Ref. [40] it is used in conjunction with a neutral vector boson singlet and
VLQs to explain also $b\to s\ell\ell$, $Z\to\bar{b}b$ and $\tau\to\mu\nu\nu$
data, while in Ref. [41] together with a scalar leptoquark to explain also
$(g-2)_{\mu}$ and charged-current B decays.

##  4 Conclusion

The observed first row CKM unitarity violation, at the $3\,\sigma$ level, has
recently been the object of a detailed study. Interpreting it as a new hint of
LFU violation allows for simple solutions of the tension and introduces
intriguing correlations with other aomalies in the flavour sector. An analysis
in terms of effective operators identifies 4 classes of possible solutions:
four-fermion operators affecting $\mu\to e\nu\nu$, four-fermion operators
affecting $d\to ue\nu$, modified $W$–$u$–$d$ couplings and modified
$W$–$\ell$–$\nu$ couplings. Taking into account the constraints from all
relevant observables, only five gauge invariant dim-6 operators, in SM
effective field theory (SMEFT), can account for the CAA anomaly:
$Q_{\ell\ell}^{2112},\,Q_{\ell q}^{\left(3\right)1111},\,Q_{\phi
ud}^{11(12)},\,Q_{\phi q}^{\left(3\right)11}$ and $Q_{\phi\ell}^{(3)22}$.
Then, performing a systematic study of simplified models which, one finds that
a vector boson singlet, a vector boson triplet, a singly charged scalar,
vector-like leptons, vector-like quarks and leptoquarks can generate these
operators. The vector boson singlet and the leptoquarks can only allevatiate
the CAA while all the other simplified models can completely solve it. In
addition, they can give rise to interesting correlations with other
observables such as $b\to s\ell\ell,\,Z\to\bar{b}b$ and $\tau\to\mu\nu\nu$.
This result suggests a common explanatory framework for several anomalies and
opens up novel and interesting avenues for model building. At the same time,
the need for developments in the extraction of $V_{us}$ and $V_{ud}$ and in
the analysis of the CAA, on the theoretical and experimental side, is
highlighted.

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# UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection
Using Generative-based and Mutation-based Data Augmentation

Amirhossein Abaskohi, Arash Rasouli, Tanin Zeraati††footnotemark: , Behnam
Bahrak
School of Electrical and Computer Engineering, College of Engineering
University of Tehran, Tehran, Iran
{amir.abaskohi, arash.rasouli, t.zeraati<EMAIL_ADDRESS>equal contribution

###### Abstract

Sarcasm is a term that refers to the use of words to mock, irritate, or amuse
someone. It is commonly used on social media. The metaphorical and creative
nature of sarcasm presents a significant difficulty for sentiment analysis
systems based on affective computing. The methodology and results of our team,
UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in
this paper. We put different models, and data augmentation approaches to the
test and report on which one works best. The tests begin with traditional
machine learning models and progress to transformer-based and attention-based
models. We employed data augmentation based on data mutation and data
generation. Using RoBERTa and mutation-based data augmentation, our best
approach achieved an F1-sarcastic of 0.38 in the competition’s evaluation
phase. After the competition, we fixed our model’s flaws and achieved an
F1-sarcastic of 0.414.

UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection
Using Generative-based and Mutation-based Data Augmentation

Amirhossein Abaskohi, Arash Rasouli††thanks: equal contribution, Tanin
Zeraati††footnotemark: , Behnam Bahrak School of Electrical and Computer
Engineering, College of Engineering University of Tehran, Tehran, Iran
{amir.abaskohi, arash.rasouli, t.zeraati<EMAIL_ADDRESS>

##  1 Introduction

Billions of internet users use social networks not only to stay in touch with
friends, meet new people, and share user-generated content but also to express
their opinions on a wide range of topics using a variety of methods such as
posting comments, videos, photos, etc. with specific groups of people
Tungthamthiti et al. (2016). In these platforms, users could submit
information on whatever topic they wanted, with no restrictions on the sort of
content they may share. The lack of constraints and individuals’ anonymity on
these networks led to humorous sarcastic texts.

Because sarcasm indicates sentiment, detecting sarcasm in a text is critical
for anticipating the text’s accurate sentiment, making sarcasm detection a
valuable tool with multiple applications in domains such as security, health,
services, product evaluations, and sales. Sarcasm detection is an essential
aspect of creative language comprehension Veale et al. (2019) and online
opinion mining Kannangara (2018). Even for humans, identifying sarcasm is
difficult due to heavily contextualized expressions Walker et al. (2012).
There are few labeled data resources for sarcasm detection. Any available
texts that can be collected (for example, Tweets) contain many issues, such as
an evolving dictionary of slang words and abbreviations, requiring many hours
of human annotation to prepare the data for any potential use. Furthermore,
the nature of sarcasm identification adds to the task’s difficulty, as sarcasm
may be considered relative and varies significantly across people, depending
on a variety of criteria such as the context, area, time, and events
surrounding the statement.

In an attempt to solve this issue, we participated in SemEval-2022 shared task
6 Abu Farha et al. (2022), which aims to recognize whether a tweet is
sarcastic or not. Our contributions are as follows:

  1. 1.

We experiment with simple machine learning models like Support Vector Machine
(SVM) and various word encodings.

  2. 2.

To discover the optimum data preprocessing method, we tested the effect of
various data preprocessing.

  3. 3.

We put several data augmentation techniques to the test.

  4. 4.

On our best dataset, we evaluated Long Short Term Memory (LSTM) based models,
Bidirectional Encoder Representations from Transformers (BERT) based models,
and attention-based models. Different neural network topologies are compared,
and the model with the highest performance is reported.

With RoBERTa (A Robustly Optimized BERT Pretraining Approach), no
preprocessing, and mutation-based data augmentation, our top result gets an
F1-sarcastic of 0.38. However, we obtain better outcomes, with a 0.414
F1-sarcastic after fixing the problems of our proposed method.

The rest of this paper is organized as follows. In Section 2, we discuss the
related work, Section 3 introduces the dataset. In Sections 4 and 5, we
present our methodology and results, respectively. Finally Section 6 concludes
the paper.

##  2 Related Work

We give a quick review of previous works on sarcasm detection in this part,
followed by works on data augmentation.

###  2.1 Sarcasm Detection on Twitter

Sarcasm detection has been represented as a binary classification issue, with
most tweets labeled with specific hashtags (e.g., #sarcasm, #sarcastic) being
considered sarcastic. Many techniques in various languages have been proposed
using this framework.

In Davidov et al. (2010), Semi-supervised sarcasm detection experiments were
done using a Twitter dataset (5.9 million tweets) and 66,000 Amazon product
evaluations. On the product review dataset, they acquired an F-measure of
0.83. On the Twitter dataset, they obtained an F-measure of 0.55 using 5-fold
cross-validation on their k-Nearest Neighbor (kNN) like classifier.

González-Ibánez et al. (2011) used 900 messages from Twitter sorted into three
groups (sarcastic, positive sentiment, and negative sentiment). To find
sarcastic tweets, they utilized the hashtags #sarcasm and #sarcastic. SVM with
Sequential Minimum Optimization (SMO) and logistic regression were employed as
classifiers. The best accuracy for the sarcastic class was 0.65.

Reyes et al. (2012) presented elements to capture ambiguity, polarity,
unexpectedness, and emotive situations in figurative language. F1-sarcastic of
0.65 was the best result in categorizing irony and general tweets.

The representativeness and significance of conceptual elements have been
investigated in Reyes et al. (2013). Punctuation marks, emoticons, quotations,
capitalized words, lexicon-based features, character n-grams, skip-grams, and
polarity skip-grams are all examples of these characteristics. Each of the
four categories (irony, comedy, education, and politics) in their corpus has
10,000 tweets. Using the Naive Bayes and decision trees algorithms, they
evaluated two distributional scenarios: balanced distribution and unbalanced
distribution (25% ironic tweets and 75% tweets from the three non-ironic
categories). The decision trees classified the balanced distribution with an
F1-sarcastic of 0.72 and the unbalanced distribution with an F1-sarcastic of
0.53.

One sort of sarcasm identified by Riloff et al. (2013) is the difference
between a good mood and a bad scenario. Using a bootstrapping approach, the
authors gathered collections of positive sentiment phrases and negative
circumstance words from sarcastic tweets. They suggested a method for
classifying tweets as sarcastic if they contain a positive predictive close to
a negative context phrase. They used a SVM classifier using unigrams and
bigrams as features to evaluate a human-annotated dataset of 3000 tweets (23%
sarcastic), getting an F1-sarcastic of 0.48. The F1-sarcastic of the hybrid
strategy, which combined the findings of the SVM classifier with their
baseline method, was 0.51.

Lukin and Walker (2017) used bootstrapping, syntactic patterns, and a high
precision classifier to classify sarcasm and nastiness in online chats. On
their snark dataset, they got an F1-sarcastic of 0.57.

In Oprea and Magdy (2019), LSTM, Att-LSTM, CNN, SIARN, MIARN, 3CNN, and Dense-
LSTM models were used to assess the task dataset that was introduced in Oprea
and Magdy (2019), which is an unbalanced dataset and labeled by the tweets’
writers. Using Multi-Dimension Intra-Attention (MIARN) Tay et al. (2018)
Network, they could get an F-score of 0.364.

In Guo et al. (2021), the Latent Optimized Adversarial Neural Transfer (LOANT)
model was suggested as a novel latent-optimized adversarial neural transfer
model for cross-domain sarcasm detection. LOANT surpasses classical
adversarial neural transfer, multitask learning, and meta-learning baselines
using stochastic gradient descent (SGD) with a one-step look-ahead and sets a
new state-of-the-art F-score of 0.4101 on the iSarcasm dataset.

###  2.2 Data Augmentation

Natural Language Processing(NLP) encompasses a wide range of tasks, from text
categorization to question answering, but no matter what you do, the quantity
of data you have to train your model has a significant influence on the
model’s performance. Using the data you already have, data augmentation
techniques are used to produce extra, synthetic data. Augmentation techniques
are widely used in computer vision applications, but they may also be used in
natural language processing.

In the instance of Twitter, Van Hee et al. (2018) and Ilić et al. (2018) found
that adding more data from the same domain did not improve the performance for
recognizing sarcasm and irony. Although their result is not general for all
sarcasm detection tasks and the result of data augmentation depends on the
data and augmentation method.

Lee et al. (2020)’s idea is to make a new datapoint out of the context
sequence [c1, c2,, cn] and label it "NOT SARCASM." The sequence could not be
identified as "SARCASM" without the answer [r1]. They believe that the newly
created negative samples will aid the model in focusing on the link between
the response [r1] and its contexts [c1, c2, cn]. They also create positive
samples using back-translation proceduresBerard et al. (2019); Zheng et al.
(2019) in French, Spanish, and Dutch to balance out the quantity of negative
examples.

In Feng et al. (2020) different data augmentation methods were tested on Yelp
Reviews datasetYel (2014) for GPT-2 generative modelRadford et al. (2019).
They used "Random Insertion, Deletion, & Swap", "Semantic Text Exchange
(STE)", "Synthetic Noise", and "Keyword Replacement". They showed in some case
data augmentation could help them to reach better performance.

This paper is the first to look at generative-based and mutation-based data
augmentation strategies in sarcasm detection.

##  3 Dataset

We mostly used the iSarcasm Oprea and Magdy (2019) dataset in this study. In
specific experiments, we integrated the primary dataset with various secondary
datasets, including the Sarcasm Headlines Dataset Misra and Arora (2019) and
Sentiment140 dataset Go et al. (2009) to increase the quantity of data and
compensate for the lack of sarcastic data. For each dataset, the details are
further discussed. It is worthy to mention that all of the supplementary
datasets we included had a negative impact on our model’s performance. We
believe this was the result of a different data gathering method. Because to
the differing labeling process and domain, the distribution diverged from that
of iSarcasm. As a result, the following sections are solely dependent on the
iSarcasm dataset, with no other datasets being used.

###  3.1 Main Task Dataset: iSarcasm

According to Oprea and Magdy (2019), the sarcasm labeling using hashtags to
build datasets captures just the sarcasm that the annotators were able to
detect, leaving out the intended sarcasm. When the author intends for the
content to be sarcastic, it is called intended sarcasm. The iSarcasm dataset
includes 4484 tweets: 3707 non-sarcastic and 777 sarcastic. Because some
tweets had been erased, we only had access to 3469 tweets for the job. The
unbalanced dataset and the scarcity of sarcastic data were two of the most
significant issues we encountered. Table 1 displays some of the dataset’s
annotated remarks.

Table 1: Example of Sarcastic and Non-Sarcastic tweets. Tweet | Sarcastic | Sarcasm Type
---|---|---
Oh my goodness. It’s the first week of the |  |
summer holidays and @name has found | Sarcastic | [’Sarcasm’]
his recorder Give.Me.Strength. |  |
90% of adulthood is just refilling your @name pitcher. | Sarcastic | [’Irony’, ’overstatement’]
True bliss is laying in an ice |  |
cold bath during the hottest part of the year | Non-Sarcastic | []

###  3.2 Sarcasm Headlines Dataset

Sarcasm Headlines Dataset Misra and Arora (2019); Misra and Grover (2021) was
gathered from two news websites. It is beneficial since it overcomes the
constraints of Twitter datasets due to noise. As the second edition of this
dataset includes more data and a greater variety of data than the first
version, we chose the second version.

###  3.3 Sentiment140 Dataset

We needed to compensate for the limited data to train our model successfully.
As a result, we chose the sentiment140 dataset Go et al. (2009) because it has
a large quantity of data and is based on Twitter. The sentiment tweet message
is labeled using an automated classification approach in this dataset. The
accuracy is more than 80% when using a machine learning algorithm.

##  4 Methodology

In this study we examined and analyzed various models and data augmentation
strategies for sarcasm detection. First, we go through data augmentation
methods; then, we discuss the structure and hyperparameters of these models in
this section. The codes of all models are available on
GitHub111https://github.com/AmirAbaskohi/SemEval2022-Task6-Sarcasm-Detection .

###  4.1 Data Augmentation

####  4.1.1 Generator-based

For this augmentation method, we used GPT-2 Radford et al. (2019) generative
model to generate 4000 tweets for both sarcastic and non-sarcastic classes.
Then we selected 2000 tweets of each class randomly to increase dataset
quantity and have more sarcastic samples.

####  4.1.2 Mutation-based

We used three distinct ways to change the data in this method: eliminating,
replacing with synonyms, and shuffling. These processes were used in the
following order: shuffling, deleting, and replacing. The removal and
replacement were carried out systematically. We used the words’ roots to
create a synonym dictionary. Synonym dictionary is created by scarping the
Thesaurus website222https://www.thesaurus.com. When a term was chosen to be
swapped with its synonyms, we chose one of the synonyms randomly (Figure 1).
We tried each combination of these processes to find the best data
augmentation combination (a total of seven).

Figure 1: Effect of shuffling, word elimination, and replacing with synonyms
on a tweet sample.

###  4.2 Models

####  4.2.1 Support Vector Machine (SVM)

We utilized SVM to discover the optimal approaches for dataset preprocessing
and word embeddings. For data augmentation, we employed both generator-based
and mutation-based methods. We also put other data preprocessing approaches to
the test, such as link removal, emoji removal, stop word removal, stemming,
and lemmatizing. We utilized TF-IDF, Word2Vec Mikolov et al. (2013), and BERT
Devlin et al. (2018) for word embedding. We found that using a regularization
value of 10 and a Radial Basis Function (RBF) kernel, BERT word embedding, and
no data preprocessing will give us the best results.

####  4.2.2 LSTM-based Methods

We begin with the intuition that a memory model can help us reach a better
result. So we started with Long Short Term Memory (LSTM) model Hochreiter and
Schmidhuber (1997). We used one LSTM layer followed by time distributed dense
layer. We repeated these two layers one more time, and then we used another
LSTM layer followed by two dense layers. This model and all of the following
models in this section were trained in 10 epochs.

In addition, we used Bidirectional Long Short Term Memory (BLSTM). Using
bidirectional will run the inputs in two directions, one from past to future
and the other from future to past. We used one BLSTM layer for this network,
followed by a time-distributed dense layer. We repeated these two layers one
more time, and then we used another BLSTM layer followed by two dense layers.

Furthermore, we combined LSTM and BLSTM with Convolutional Neural Networks
(CNNs). CNN layers for feature extraction on input data are paired with LSTM
to facilitate sequence prediction in the CNN-LSTM architecture. Although this
model is often employed for video datasets, Rehman et al. (2019) demonstrated
that it could perform better in sentiment analysis tasks. We used three 1D
convolutional layers followed by a 1D global max-pooling layer for the
convolutional part. We used these layers at the end of LSTM-based networks.

####  4.2.3 BERT-based Methods

The use of bidirectional training of transformer and a prominent attention
mode for language modeling is BERT’s fundamental technological breakthrough
Devlin et al. (2018). The researchers describe a new Masked Language Model
(MLM) approach that permits bidirectional training in previously tricky
models. They found that bidirectionally trained language models can have a
better understanding of language context and flow than unidirectional ones.

Robustly Optimized BERT or RoBERTa has a nearly identical architecture to
BERT, however, the researchers made some minor adjustments to its architecture
and training technique to enhance the results on BERT architecture Liu et al.
(2019).

We used both RoBERTa with twitter-roberta-base, which has been trained on near
58 million tweets and finetuned for sentiment analysis with the TweetEval
benchmark and BERT with bert-base from Huggingface Wolf et al. (2019). For
both models, we employed five epochs, batch size of 32, 500 warmup steps, and
a weight decay of 0.01.

####  4.2.4 Attention-based Methods

One of the most important achievements in deep learning research in the recent
decade is the attention mechanism Vaswani et al. (2017). The Encoder-Decoder
model’s restriction of encoding the input sequence to one fixed-length vector
to decode each output time step is addressed via an attention mechanism. This
difficulty is thought to be more prevalent when decoding extended sequences.

We start with the assumption that if a model with an attention layer is
trained to identify sarcasm at the sentence level, the sarcastic words will be
the ones the attention layer learns to value. As a result, we added an
attention layer to our LSTM-based and BERT-based models. The results will be
discussed further.

####  4.2.5 Google’s T5

Google’s T5 Raffel et al. (2019) text-to-text model outperformed the human
baseline on the GLUE, SQuAD, and CNN/Daily Mail datasets and earned a
remarkable 88.9 on the SuperGLUE language benchmark.

We fine-tuned T5 for our problem and dataset by giving the sarcastic label the
target and the tweets as the source. We used two epochs, batch size of 4, 512
tokenization max length, Adam epsilon of 1e-8, word decay of 0, no warmup
steps, and learning rate of 3e-4 (Figure 2)333We were not able to test a
larger version of the model with better hyperparameters due to resource
constraints.

Figure 2: Fine-tuning T5 model for sarcasm detection problem.

##  5 Results

In this section we report the results of our models introduced in Section 4.

It’s important to note that after the competition, we discovered that none of
our preprocessing strategies improved the performance of our model. So we were
able to get an F1-sarcastic of 0.414 without using any preprocessing methods,
which was 0.034 higher than our performance in the competition, which was
based on the best combination of preprocessing methods.

Table 2: F1-sarcastic and accuracy for different data augmentation methods on SVM model with BERT word embedding and no preprocessing. Data Augmentation | F1-sarcastic | Accuracy
---|---|---
Shuffling | 0.305 | 0.7471
Shuffling + Replacing | 0.301 | 0.741
Shuffling + Removing | 0.306 | 0.747
Removing | 0.301 | 0.747
GPT-2 | 0.292 | 0.675

###  5.1 Support Vector Machine (SVM)

The optimum augmentation technique, preprocessing method, and word embedding
were all determined using the SVM model. Without any augmentation, BERT
obtained the greatest F1-sarcastic of 0.2862, compared to 0.2541 and 0.0924
for Word2Vec and TF-IDF, respectively.

We have also looked at several ways of data augmentation. The F1-sarcastics
for shuffling with replacing words, only word elimination, just shuffling, and
shuffling with word elimination were the highest in the mutation-based
augmentation (Table 2). We also tried these data augmentation and GPT-2 data
augmentation on RoBERTa because the results were close, and we found that
merely word removal was the best data augmentation. The following results are
based on no data preprocessing, BERT word embedding, and mutation-based data
augmentation utilizing only word removal.

###  5.2 LSTM-based Methods

LSTM obtained an F1-sarcastic of 0.2176 using BERT word embeddings, mutation-
based data augmentation, and no preprocessing, whereas BLSTM’s F1-sarcastic
was 0.2439 using BERT word embeddings, mutation-based data augmentation, and
no preprocessing. By adding CNN layers, the F1-sarcastic of the LSTM was
increased to 0.2453, and the BLSTM was increased to 0.2751. The CNN model’s
F1-sarcastic was 0.2263.

###  5.3 BERT-based Methods

We employed a mutation-based data augmentation approach with no preprocessing
for BERT-based procedures. We got an F1-sarcastic of 0.323 using BERT. We
achieved our best result with RoBERTa with an F1-sarcastic of 0.414, which was
better than LOANT Guo et al. (2021) model on the same dataset.

Table 3: Best results for each model using iSarcasm dataset and mutation-based data augmentation. Model | F1-sarcastic | Accuracy
---|---|---
SVM | 0.3064 | 0.7478
LSTM-based | 0.2751 | 0.7251
BERT-based | 0.414 | 0.8634
Attention-based | 0.2959 | 0.7793
Google’s T5 | 0.4038 | 0.8124

###  5.4 Attention-based Methods

Adding attention layers to this job was not helpful, and it decreased our
models’ performance. RoBERTa’s F1-sarcastic dropped to 0.2959 using the
attention layer. LSTM model with the attention layer earned an F1-sarcastic of
0.2145. The F1-sarcastic of BLSTM with attention layer was 0.2336.

###  5.5 Google’s T5

Based on the hyperparameters listed in the Section 4, our F1-sarcastic for
this model is 0.4038. However, we believe that we may get better results by
increasing the tokenization max length, increasing the batch size, and
utilizing the t5-large pre-trained model.

##  6 Conclusion

In this study, we reviewed and contrasted a number of sarcasm detection
methods. To improve the performance of our model, we experimented with two
different types of augmentation. In the job of sarcasm detection, we observed
that mutation-based data augmentation can assist us in achieving better
results than generative-based data augmentation. Additionally, we tested with
other deep-learning techniques, including RNN and BERT-based models. Our best
system, an ensemble model, has an F1-sarcastic of 0.414.

## Acknowledgements

We want to convey our heartfelt gratitude to Prof. Yadollah Yaghoobzadeh, who
provided us with invaluable advice during our research.

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