# Perturbation-based Self-supervised Attention
for Attention Bias in Text Classification

Huawen Feng, Zhenxi Lin, Qianli Ma Huawen Feng, Zhenxi Lin, and Qianli Ma are
with the School of Computer Science and Engineering, South China University of
Technology, Guangzhou, 510006, China (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>*corresponding author)

###### Abstract

In text classification, the traditional attention mechanisms usually focus too
much on frequent words, and need extensive labeled data in order to learn.
This paper proposes a perturbation-based self-supervised attention approach to
guide attention learning without any annotation overhead. Specifically, we add
as much noise as possible to all the words in the sentence without changing
their semantics and predictions. We hypothesize that words that tolerate more
noise are less significant, and we can use this information to refine the
attention distribution. Experimental results on three text classification
tasks show that our approach can significantly improve the performance of
current attention-based models, and is more effective than existing self-
supervised methods. We also provide a visualization analysis to verify the
effectiveness of our approach.

###### Index Terms:

Attention bias, perturbation, self-supervised learning, text classification.

##  I Introduction

Attention mechanisms [1, 2, 3] play an essential role in Natural Language
Processing (NLP) and have been shown to be effective in various text
classification tasks, such as sentiment analysis [4, 5, 6], document
classification [7] and natural language inference [8]. They achieve
significant performance gains, and can be used to provide insights into the
inner workings of the model. Generally, the attention learning procedure is
conditioned on access to large amounts of training data without additional
supervision information.

Although the current attention mechanisms have achieved remarkable
performance, several problems remain unsolved. First, learning a good
attention distribution without spurious correlations for neural networks
requires large volumes of informative labeled data [9, 10]. As described in
the work of Wallace et al. [11], after inserting 50 poison examples with the
name “James Bond” into its training set, a sentiment model will frequently
predict a positive whenever the input contains this name, even though there is
no correlation between the name and the prediction. Second, attention
mechanisms are prone to focus on high-frequency words with sentiment
polarities and assign relatively high weights to them [12, 13, 5], while the
higher frequency does not imply greater importance.

Especially when there’s an adversative relation in a text, some high-frequency
words with strong sentiment valence need to be selectively ignored based on
the context of the whole text. In these cases, these words will mislead the
model because the important words don’t get enough attention. The sentences in
Figure 1 illustrate this problem. In most training sentences, as shown in the
first four rows, “better” and “free” appear with positive sentiment, which
makes the attention mechanism accustomed to attaching great importance to them
and relating them to positive predictions. However, the two words are used
ironically in the fifth sentence, and the model pays the most attention to
them while the critical word – “leave” – is not attended to, resulting in an
incorrect prediction. Based on these observations, there’s reason to believe
that the attention mechanisms could be improved for text classification.

Figure 1: The attention visualization for five sentences. The ”A/B” style tags
before each row mean the model’s prediction is A and the label is B. The first
four sentences are selected from training sets as representatives containing
high-frequency words - ”better” (yellow box) and ”free” (green box). The last
sentence including both of the two words is selected from testing sets,
typically showing that the distribution of attention weights when some words
in the sentence appear frequently in the corpus but are unimportant to the
current prediction.

To tackle this problem the most direct solution is to add human supervision
collected by manual annotation [14, 10, 15] or special instruments [9, 16, 17,
18] (e.g., eye-tracking), to provide an inductive bias for attention. These
approaches are costly, the labeling is entirely subjective, and there is often
high variance between annotators. In particular, Sen et al. [19] point out
that there is a huge difference between machine and human attention and it is
difficult to map human attention to machine attention.

Another flexible solution is to measure attribution scores, i.e., how much
each token in a text contributes to the final prediction, to approximate an
importance distribution as an attention supervision signal [20, 21, 5, 6].
Generally, the attribution scores are obtained by masking each token one by
one to generate counterfactual examples, reflecting the difference in the
softmax probability of the model after masking each token. These approaches
have little or no additional annotation overhead and augment supervision
information from the training corpus to refine the attention distribution.
Despite their success, masking schemes can give rise to an out-of-distribution
(OOD) problem [22, 23, 24]. That is, the generated counterfactuals deviate
from the training data distribution of the target model, resulting in an
overestimation of the contribution of unimportant tokens. The OOD problem
induced by existing masking schemes makes it difficult to identify whether
high-scoring tokens contribute significantly to the prediction. Furthermore,
most of them are limited to generating uniform attention weights for the
selected important words. Obviously, the contribution of different important
words to the model should also be different according to the context, e.g.,
the word leave should have a higher attention weight than better and free for
the fifth sentence in Figure 1.

Some efforts reveal that the output of neural networks can be theoretically
guaranteed to be invariant for a certain magnitude of input perturbations
through establishing the concept of maximum safety radius [25, 26] or minimum
disturbance rejection [27]. In simple terms, these approaches evaluate the
minimum distance of the nearest perturbed text in the embedding space that is
classified differently from the original text. Inspired by this work, we
propose a novel perturbation-based self-supervised attention learning method
without any additional annotation overhead for text classification.
Specifically, we design an attention supervision mining mechanism called Word-
based Concurrent Perturbation (WBCP), which effectively calculates an
explainable word-level importance distribution for the input text. Concretely,
WBCP tries to concurrently add as much noise as possible to perturb each word
embedding of the input, while ensuring that the semantics of input and the
classification outcome is not changed. Under this condition, the words that
tolerate more noise are less important and the ones sensitive to noise deserve
more attention. We can use the permissible perturbation amplitude as a measure
of the importance of a word, where small amplitude indicates that minor
perturbations of that word can have a significant influence on the semantic
understanding of input text and easily lead to prediction error.

According to the inverse distribution of perturbation amplitude, we can get
sample-specific attention supervision information. Later, we use this
supervision information to refine the attention distribution of the target
model and iteratively update it. Notably, our method is model-agnostic and can
be applied to any attention-based neural network. It generates attention
supervision signals in a self-supervised manner to improve text classification
performance without any manual labeling and incorporates Perturbation-based
Self-supervised Attention (PBSA) to avoid the OOD problem caused by the
masking scheme. In addition, it can also generate special attention
supervision weights adaptively for each sample based on the perturbation
amplitude, rather than allocate them uniformly.

In summary, the contributions of this paper are as follows:

(1) Through analysis of current methods, we point out the disadvantages and
drawbacks of current attention mechanisms for text classification.

(2) We propose a simple yet effective approach to automatically mine the
attribution scores for the input text, and use it as supervision information
to guide the learning of attention weights of target models.

(3) We apply our approach to various text classification tasks, including
sentence classification, document categorization, and aspect-level sentiment
analysis. Extensive experiments and visualization analysis show the
effectiveness of the proposed method in improving both model prediction
accuracy and robustness.

(4) Theoretically, our algorithm can be applied to the models with attention
mechanisms, but it is impossible to compare with all of them. Considering
this, we conduct our experiments on several typical baselines (LSTM, BERT
[28], DEBERTA [29], ELECTRA [30], Memory Net [31], etc.) to justify the
effectiveness of our method. Notably, we also compared our algorithm with
other advanced attention self-supervised methods (PGAS [32], AWAS [5], SANA
[6]).

##  II Related work

Figure 2: The diagram of WBCP. The left part of the figure corresponds to the
last term of Eq. (2), which illustrates the process of adding noise that
follows a Gaussian distribution to each word. The right part of the figure
corresponds to the first two terms of Eq. (2), indicating the constraint of
trying to not change the semantics and predictions after the noise is
introduced.

Work related to our method can be categorized into three types: Introducing
human attention; using external resources or tools; and using self-
supervision.

Introducing human attention Adding human supervision to attention has been
shown to effectively alleviate attention bias and improve model prediction
accuracy on a range of tasks [14, 15, 16, 17, 18]. In general, the annotators
need to explicitly highlight the important words or rationales [14, 10, 15]
for the given sample. Obviously, the annotation is very labor-intensive and
expensive in real-world scenarios, so an alternative is to use implicit
signals such as eye gaze [9, 16, 17, 18]. For these methods, it is expected
that the model can generate similar attention to human supervision. However,
human recognition and model reasoning processes may be inconsistent [33], and
aligning the two is challenging [19].

Using external resources or tools With the development of NLP, many corpora
and tools, such as Dependency Tree and Synonym Dictionary, are created to
obtain a deeper understanding of words and sentences. Therefore, some methods
[34, 35, 36, 37] that generate attention supervision information according to
existing corpora and tools emerge. For example, Nguyen et al. [36] introduce
attention supervision information based on important words selected by
semantic word lists and dependency trees. Similarly, Zhao et al. [37] first
train the model on the document-level sentiment classification and then
transfer the attention knowledge to a fine-grained one for aspect-level
sentiment classification. And Hu et al. [38] introduce the tree structure’s
representation into attention computations. However, these methods still rely
on annotations based on parsers or external resources, and the performance
depends heavily on the quality of the parser.

Self-supervised attention learning Currently, self-supervised attention
learning frameworks [20, 21, 5, 6, 32] have become the mainstream method
because they do not require additional annotation overhead. They usually mask
or erase each token one by one and quantify the difference in predictions of
the model after masking each token, to approximate an importance distribution
as attention supervision information. For example, Tang et al. [5] divide the
words in sentences into the active set and the misleading set by progressively
masking each word with respect to the maximum attention weight, and augment
them to make the model focus on the active context words. Similarly, Choi et
al. [6] adopt the masking method to find the unimportant words and gradually
reduce their weights. These methods use a self-supervised paradigm to mine
important words, which can greatly reduce the annotation cost and improve the
robustness of the model. Nevertheless, the masking scheme they follow has an
OOD problem. The counterfactuals generated by the mask operation deviate from
the original training set distribution, which easily leads to the over-
evaluation of unimportant words. In addition, the above methods usually assign
the same weight to the extracted important words, but in our opinion,
different words should have different contributions to the classification.

##  III Proposed method

In this section, we propose a Perturbation-based Self-supervised Attention
(PBSA) mechanism to enhance the attention learning process and provide a good
inductive bias. We first design a Word-based Concurrent Perturbation (WBCP) to
automatically mine the attribution score for each word and use this as a
measure of its degree of importance. Then we use the measure mentioned above
to compute a word-level importance distribution as supervision information.
Finally, we describe how to use the supervision information to refine the
attention mechanism of the target model, improving the accuracy and robustness
of text classification tasks.

###  III-A Word-based Concurrent Perturbation

The basic assumption of our design is based on the following fact: under the
premise of trying not to change the semantics of the input text, unimportant
words can withstand more changes than more significant ones. Specifically, a
little noise on keywords can lead to dramatic changes in the final results,
while greater noise on the unimportant ones won’t easily lead to changes in
results. Therefore, we can estimate the importance distribution of the words
according to the maximum amount of noise they can tolerate. To be specific, we
try to concurrently add as much noise as possible to perturb each word
embedding without changing the latent representations (e.g., the hidden states
for classification) of the text and the prediction result. The above process
can be optimized according to the maximum entropy principle.

Given a sentence consisting of $n$ words $s=\\{w_{1},w_{2},...,w_{m}\\}$, we
map each word into its embedding vector
$\boldsymbol{X}=\\{\boldsymbol{x}_{1},\boldsymbol{x}_{2},...,\boldsymbol{x}_{n}\\}$.
Actually, WBCP (Word-based Concurrent Perturbation) is based on the embedding
of each token $\boldsymbol{X}$ but not each word $s$. Intuitively, one word
can be tokenized into several parts, and various parts have various influences
on the representation. Considering that, in experiments, the perturbation is
added to each token generated by the tokenizer, which means each token has its
own $\sigma_{i}$ (maximum safety radius). For ease of explanation and
comprehension, here we take the traditional embedding where $m=n$ (each word
has only one embedding, e.g. word2vec, glove, and so on) as an example in
Figure 2 and Section III-A. We assume that the noise on word embeddings obeys
a Gaussian distribution
$\boldsymbol{\epsilon}_{i}\sim\mathcal{N}\left(\mathbf{0},\mathbf{\Sigma}_{i}=\sigma_{i}^{2}\mathbf{I}\right)$
and let
$\widetilde{\boldsymbol{x}_{i}}=\boldsymbol{x}_{i}+\boldsymbol{\epsilon}_{i}$
denote an input with noise $\boldsymbol{\epsilon}_{i}$. We use
$\boldsymbol{h}$, $\boldsymbol{y}$ and $\widetilde{\boldsymbol{h}}$,
$\widetilde{\boldsymbol{y}}$ to indicate the hidden state for classification
and the prediction result of a pre-trained model with no noise and with noise
respectively. Then we can write the loss function of WBCP as follows:

$\displaystyle\begin{split}\mathcal{L}_{WBCP}=||\widetilde{\boldsymbol{h}}-\boldsymbol{h}||^{2}_{2}+||\widetilde{\boldsymbol{y}}-\boldsymbol{y}||^{2}_{2}\\\
-{\lambda}{\sum}_{i=1}^{n}H({\boldsymbol{\epsilon}_{i}})|_{\boldsymbol{\epsilon}_{i}\sim\mathcal{N}\left(\mathbf{0},\mathbf{\Sigma}_{i}=\sigma_{i}^{2}\mathbf{I}\right)},\end{split}$
(1)

where $\lambda$ is a hyperparameter that balances the strength of noise.

The first and the second term of Eq. (1) mean that we need to minimize the
L2-normalized euclidean distance between the two hidden states and between the
two predictions respectively, to quantify the change of information [39]. The
first term maintains latent representations to prevent modification of the
text semantics, and the second term prevents excessive perturbations from
causing the model to mispredict. The last term indicates that we need to
maximize the entropy
$H(\boldsymbol{\epsilon}_{i})|_{\boldsymbol{\epsilon}_{i}\sim\mathcal{N}\left(\mathbf{0},\mathbf{\Sigma}_{i}=\sigma_{i}^{2}\mathbf{I}\right)}$
to encourage adding as much noise as possible to each word embedding. We can
simplify the maximum entropy of the Gaussian distribution as follows:

$\displaystyle Maximize(H({\boldsymbol{\epsilon}}_{i}))$
$\displaystyle=Maximize(-\int p({\boldsymbol{\epsilon}}_{i})\ln
p({\boldsymbol{\epsilon}}_{i})d{\boldsymbol{\epsilon}}_{i})$
$\displaystyle=Maximize(\frac{1}{2}(\ln(2{\pi}{\sigma_{i}}^{2})+1))$
$\displaystyle=Maximize(\ln 2(\frac{1}{2}\log(2\pi e)+\log{\sigma}_{i}))$
$\displaystyle=Maximize(\log{\sigma}_{i})$

Finally we can use Eq. (III-A) to rewrite our final objective function:

$\begin{split}\mathcal{L}_{WBCP}=||\widetilde{\boldsymbol{h}}-\boldsymbol{h}||^{2}_{2}+||\widetilde{\boldsymbol{y}}-\boldsymbol{y}||^{2}_{2}+{\lambda}{\sum}_{i=1}^{n}\log({-\sigma_{i}})\end{split}$
(2)

The illustration of WBCP is given in Figure 2. After fixing the parameters of
the pre-trained model, the only learnable parameters
${\sigma}=\\{{\sigma}_{1},{\sigma}_{2},{\sigma}_{3},{\sigma}_{4}\\}$ can be
considered as the perturbation radii, which is positively associated with the
perturbation amplitude. Specifically, the larger ${\sigma}_{i}$ WBCP gets, the
more likely ${\boldsymbol{\epsilon}}_{i}$ is a big number, the more noise is
added to $\boldsymbol{x}_{i}$, and the less important it is. As what is shown
in the picture, it is obvious that
${\sigma}_{2}>{\sigma}_{1}>{\sigma}_{4}>{\sigma}_{3}$. According to the
analysis listed above, we know that $\boldsymbol{w}_{2}$ (a) is the least
important word and $\boldsymbol{w}_{3}$ (nice) is the most significant one,
for $\boldsymbol{x}_{2}$ can tolerate the most noise while
$\boldsymbol{x}_{3}$ can hardly stand any perturbation.

During the training stage of WBCP, $\sigma$ is first initialized as the normal
distribution and then normalized by the standard deviation of sentence
embeddings before generating noise. And we set the epochs to $500$ for most
datasets. Actually, most perturbation models converge within less than $200$
steps, but we choose more epochs for the time cost is acceptable. However,
IMDB’s settings differ because of the large training and testing set.
Therefore, we set epochs to 300 for it. As for the optimizer, we select AdamW
with a learning rate of 0.01.

###  III-B Attention supervision

We obtain the $\sigma$s, the perturbation magnitudes, by optimizing Eq. (2) on
the pre-trained model. If a word embedding $\boldsymbol{x}_{i}$ can tolerate
more noise without impacting the semantics of input text, $\sigma_{i}$ will be
larger, which means the word $\boldsymbol{x}_{i}$ is less important.
Conversely, small $\sigma_{i}$ indicates that slight perturbations of word
embedding $\boldsymbol{x}_{i}$ will lead to semantic drift and may affect the
classification result. We can therefore use the perturbation magnitude to
compute a word-level importance distribution as attention supervision
information, as shown below:

$\displaystyle\alpha^{\prime}_{i}$
$\displaystyle=1-\frac{\sigma_{i}}{\text{max}_{j}\\{\sigma_{j}\\}}$ (3)
$\displaystyle{\boldsymbol{\widetilde{\alpha}}}$
$\displaystyle=\text{Softmax}({\boldsymbol{\alpha^{\prime}}})$

It is worth noting that our method generates sample-specific attention
supervision, where the weight of each word is quantified according to the
perturbation magnitude, instead of using the same importance weight for all
words [5, 6]. Also, the quantification occurs in the embedding space rather
than replacing the token with a predefined value, thus avoiding the OOD
problem caused by masking schemes.

Input: training dataset $D$, attention-based model $f(\cdot,\theta)$, the
number of iterations $T$.

Pre-train model $f(\cdot,\theta)$ on $D$ and update $\theta$ using Adam.

for  _$t=1,...T$_ do

Fix $\theta$, and minimize WBCP objective function by Eq. (2) using Adam.

Obtain the perturbation amplitude $\sigma$ for each sample in $D$.

Calculate the attention supervision $\widetilde{\alpha}$ by Eq. (3) for each
sample in $D$.

Re-train model on $D$ with the attention supervision $\widetilde{\alpha}$ by
Eq. (4) and update $\theta$ using Adam.

end for

Algorithm 1 Perturbation-based self-supervised attention

###  III-C Perturbation-based Self-supervised Attention

We do not use $\boldsymbol{\widetilde{\alpha}}$ to generate a new attention
distribution to replace the original one $\boldsymbol{{\alpha}}$. Rather, we
use it as a supervision target for the attention weights. We want the
attention supervision to make the model notice more words that have an
influence on the output. In this way, some low-frequency context words with
great importance that would normally be ignored can be discovered by attention
learning. In this section, we describe how to exploit the supervision
information $\widetilde{\alpha}$ to guide the learning of model attention
strengths.

TABLE I: Detailed dataset statistics.

Task | Dataset | Class | AvgLen | Train | Test
---|---|---|---|---|---
Sentence Classification | SST2 [40] | 2 | 19 | 6,920 | 1821
TREC [41] | 6 | 10 | 5,452 | 500
MR [42] | 2 | 19 | 10,662 | –
CR [43] | 2 | 19 | 3,775 | –
SUBJ [44] | 2 | 23 | 10,000 | –
MPQA [45] | 2 | 3 | 10,606 | –
Document Categorization | IMDB [46] | 2 | 280 | 25,000 | 25,000
Aspect-based Sentiment Analyis | REST [47] | 3 | 16 | 3,591 | 1,121
LAPTOP [47] | 3 | 17 | 2,292 | 639
TWITTER [48] | 3 | 19 | 6,248 | 692

TABLE II: Setup for Att-BiLSTM and Memory Net

Task | Dataset | Dimension of hidden states | Dimension of attention context
---|---|---|---
Sentence Classification | SST2 [40] | 150 | 100
TREC [41] | 150 | 50
MR [42] | 150 | 100
CR [43] | 150 | 50
SUBJ [44] | 150 | 100
MPQA [45] | 150 | 100
Document Categorization | IMDB [46] | 150 | 300
Aspect-based Sentiment Analyis | REST [47] | 300 | 300
LAPTOP [47] | 300 | 300
TWITTER [48] | 300 | 300

Our method is shown in Algorithm 1. We first pre-train an attention-based
model $f(\cdot,\theta)$ based on the classification dataset $D$. We then fix
the model parameters $\theta$ and minimize the WBCP objective using Eq. (2) to
obtain the perturbation amplitude $\sigma$ for each sample, and used to
compute the attention supervision $\widetilde{\alpha}$ using Eq. (3). We then
retrain the model using $\widetilde{\alpha}$ to guide the attention
distribution $\alpha$ produced by the model. The above process can iterate $T$
times to capture the important distribution more accurately. The training
objective function with attention supervision $\widetilde{\alpha}$ is defined
as follows:

$\displaystyle\begin{split}\mathcal{L}_{cls}=\frac{1}{M}{\sum}^{M}_{m=1}\hat{y}_{m}\log
y_{m}+\gamma\text{KL}(\widetilde{\alpha}_{m}||\alpha_{m}),\end{split}$ (4)

where $M$ is the number of samples, $\gamma$ is a hyperparameter that controls
the strength of attention supervision, $\hat{y}_{m}$ and $y_{m}$ are the
ground-truth label and predicted output for the $m$-th sample respectively.
The first term is the Cross-Entropy Loss for classification, and the second
term is the Kullback–Leibler Divergence between the distributions of attention
$\alpha_{m}$ produced by model and attention supervision information
$\widetilde{\alpha}_{m}$ for the $m^{th}$ sample.

It’s worth noting that our method requires extra computations, but the time
cost is usually acceptable because nearly all the process is parallel. The
analysis are explained in Appendix A.

##  IV Experiments

We tried PBSA on several text classification tasks, including sentence
classification, document categorization, and aspect-level sentiment analysis.
Experimental results demonstrate that PBSA consistently enhances the
performance and robustness of various attention-based baselines, and
outperforms some strong models following self-supervised attention learning.
Furthermore, a visualization analysis confirms that our model is capable of
generating high-quality attention for target tasks. We aim to answer the
following questions:

  * RQ1: Does PBSA improve model accuracy?

  * RQ2: Is PBSA more effective than other approaches?

  * RQ3: How do hyperparameters affect the results?

  * RQ4: How does PBSA work?

###  IV-A Datasets and Baselines

The statistics of widely-studied datasets used by different tasks are listed
in Table I. These datasets come from different topics, such as movie reviews,
customer reviews, social reviews, and question type. In particular, since
there is no standard partition of MR, CR, SUBJ, and MPQA, we follow the data
splitting protocol, 7:1:2 for them to get the training, validation, and test
sets. For the aspect-level tasks, we remove the instances with conflict
sentiment labels in Laptop and Restaurant as implemented in [49].

As for hyperparameters, we use a grid search to find the optimal value of
$\gamma$ and $T$ for each dataset, from the sets
$\gamma\in\\{0.05,0.1,1.0,2.0,10,100\\}$ and $T\in\\{1,2,3,4\\}$. We use the
Adam optimizer with learning rate 0.001 and the batch size is set to 64.

We use Att-BiLSTM, Memory Network, BERT, DEBERTA, ELECTRA, Att-BERT, BERTABSA,
Att-BERTABSA as baselines and explain the details about them in Appendix B.

The setup of hyperparameters for Att-BiLSTM and Memory Net are listed in Table
II. To make a fair compare with other algorithms, we set our hyperparameters
the same as theirs.

TABLE III: The performance of PBSA on the document-level and sentence-level
classification.

Model | IMDB | SST2 | TREC | MR | CR | SUBJ | MPQA | Average
---|---|---|---|---|---|---|---|---
Att-BiLSTM | 87.21 | 83.42 | 90.60 | 77.04 | 76.82 | 89.82 | 70.59 | 82.20
Att-BiLSTM+PBSA | 89.14 | 85.72 | 92.20 | 79.05 | 77.64 | 90.53 | 71.31 | 83.65
Att-BERT(base) | 92.53 | 91.43 | 96.60 | 79.26 | 89.06 | 94.30 | 89.69 | 90.41
Att-BERT(base)+PBSA | 92.61 | 91.93 | 97.20 | 79.97 | 89.38 | 94.76 | 90.21 | 90.86
BERT(base) | 92.92 | 91.71 | 96.60 | 85.47 | 89.42 | 96.30 | 89.59 | 91.71
BERT(base)+PBSA | 93.48 | 92.20 | 97.80 | 86.08 | 90.21 | 97.50 | 90.57 | 92.55
DEBERTA(base) | 91.14 | 92.69 | 96.20 | 86.64 | 91.01 | 95.30 | 89.74 | 91.82
DEBERTA(base)+PBSA | 91.68 | 93.02 | 96.80 | 87.18 | 91.80 | 95.70 | 90.82 | 92.43
ELECTRA(base) | 93.48 | 94.67 | 96.8 | 89.27 | 92.2 | 96.75 | 90.9 | 93.44
ELECTRA(base)+PBSA | 93.87 | 95.43 | 97.40 | 89.88 | 92.99 | 97.30 | 91.55 | 94.06

TABLE IV: Experimental accuracy on the document-level and sentence-level
classification compared with others

Model | IMDB | SST2 | TREC | MR | CR | SUBJ | MPQA | Average
---|---|---|---|---|---|---|---|---
Att-BiLSTM | 87.21 | 83.42 | 90.60 | 77.04 | 76.82 | 89.82 | 70.59 | 82.20
+Gradient | 86.79 | 85.06 | 91.20 | 77.60 | 76.54 | 89.82 | 70.76 | 82.53
+SANA [6] | 88.03 | 84.35 | - | - | - | - | - | -
+PBSA | 89.14 | 85.72 | 92.20 | 79.05 | 77.64 | 90.53 | 71.31 | 83.65

TABLE V: The performance of PBSA on the aspect-level classification.

Models | REST | LAPTOP | TWITTER
---|---|---|---
| Accuracy | Macro-F1 | Accuracy | Macro-F1 | Accuracy | Macro-F1
MN [50] | 77.32 | 65.88 | 68.90 | 63.28 | 67.78 | 66.18
MN (Ours) | 79.89 | 65.89 | 72.68 | 61.97 | 68.34 | 66.23
+PBSA | 83.98 | 70.84 | 75.75 | 67.21 | 72.10 | 69.64
BERTABSA | 79.80 | 71.37 | 79.38 | 75.69 | 76.01 | 74.52
+PBSA | 79.89 | 71.59 | 79.51 | 75.87 | 76.11 | 74.69
Att-BERTABSA | 83.29 | 75.87 | 77.98 | 75.02 | 73.99 | 71.23
+PBSA | 83.41 | 76.70 | 78.65 | 75.53 | 74.45 | 72.88

TABLE VI: Experimental results on aspect-level tasks compared with others.

Models | REST | LAPTOP | TWITTER
---|---|---|---
| Accuracy | Macro-F1 | Accuracy | Macro-F1 | Accuracy | Macro-F1
MN [50] | 77.32 | 65.88 | 68.90 | 63.28 | 67.78 | 66.18
MN (Ours) | 79.89 | 65.89 | 72.68 | 61.97 | 68.34 | 66.23
+Gradient [51] | 76.85 | 60.06 | 71.11 | 63.53 | 67.77 | 64.91
+AWAS [5] | 78.75 | 69.15 | 70.53 | 65.24 | 69.64 | 67.88
+Boosting [32] | 77.66 | 66.23 | 69.28 | 64.17 | 68.14 | 67.12
+Adaboost [32] | 76.77 | 62.29 | 67.88 | 60.52 | 66.96 | 65.09
+PGAS [32] | 78.98 | 69.42 | 70.84 | 65.58 | 69.78 | 67.80
+PBSA | 83.98 | 70.84 | 75.75 | 67.21 | 72.10 | 69.64

###  IV-B RQ1: Sentence-level and Document-level Classification

Figure 3: The visualization result of several samples on SST2 test set.

To verify that PBSA can improve the performance of the attention-based model,
in this section, we use the classic Att-BiLSTM [52] and the pre-trained models
BERT [28], DEBERTA [29], and ELECTRA [30] as the baselines. It is worth noting
that Transformers use multiple-layer and multiple-head attention, so selecting
the suitable head as the supervised target is difficult [32]. Hence, how to
effectively combine its multiple-layer and multiple-head attention with our
method is an exciting and valuable question.

The previous researchers have yet to find a good way to apply their methods to
Transformers, and we have made some explorations in this field, which is also
one of our innovations. We explore two simple strategies to combine our
approach with Transformers, 1) We first add a scaled dot-product attention
layer to the output of BERT to derive a fixed-sized sentence representation
for classification, and we call this model Att-BERT for short. 2) We also try
a simple but effective way to combine the internal multi-head attention in
Transformers with our method. Specifically, we average the multi-head
attention of all the layers and compress the attention matrix to a vector to
be guided by our mechanism.

Table III reports the experimental results on the seven datasets of sentence
classification and document categorization. We observe that our method
consistently helps improve the accuracy of the baseline on all the datasets.
The average accuracy of our approach on the five baselines across seven
datasets are 83.65, 90.86, 92.55, 92.43, and 94.06, an improvement of 1.44%,
0.45%, 0.83%, 0.66%, and 0.66% over the baselines (82.21, 90.41, 91.71, 91.82,
and 93.44). The results demonstrate that our approach delivers significant
performance improvements over the baselines. It also indicates that the
current model limits the potential of attention mechanisms when without any
supervision information. However, PBSA can mine the potential important words
and then guide the attention mechanism of the model to learn a good inductive
bias.

However, we find the improvements on pre-trained models are relatively
marginal compared with smaller models like Att-BiLSTM. The phenomenon
indicates that the pre-training on large corpora relieves the attention bias
to some extent, which is further verified in Section IV-D. Moreover, we also
find the size of the pre-trained model also impacts the performance of PBSA.
We conduct the experiments on BERT-small and ELECTRA-small (shown in Table
VII), and PBSA gains greater improvements under the same settings. To sum up,
the attention bias may be more likely to appear in smaller-size models and
smaller-scaled datasets. And the performance of PBSA will be more significant
in these scenarios.

TABLE VII: The performance of PBSA on small-size pre-trained models.

Model | IMDB | SST2 | TREC | MR | CR | SUBJ | MPQA | Average
---|---|---|---|---|---|---|---|---
BERT(small) | 90.81 | 88.85 | 95.60 | 81.16 | 81.61 | 94.45 | 87.23 | 88.53
BERT(small)+PBSA | 91.73 | 90.17 | 97.40 | 82.33 | 83.07 | 96.20 | 88.54 | 89.92
ELECTRA(small) | 92.37 | 91.21 | 96.00 | 83.60 | 87.30 | 95.70 | 88.97 | 90.74
ELECTRA(small)+PBSA | 93.35 | 92.20 | 96.40 | 84.72 | 88.62 | 96.65 | 90.62 | 91.79

Figure 4: The chart of the fluctuations of accuracy when we change the value
of the sample ratio. Each triangle point and circular point corresponds to the
accuracy of BERT and BERT+PBSA under the current sample ratio, respectively.

###  IV-C RQ1: Aspect-level Sentiment Analyis

To further verify the effectiveness of our approach, we apply PBSA into MN
[31, 50], BERTABSA [53], and Att-BERTABSA [32]. Both BERTABSA and Att-BERTABSA
are typical and simple ways to apply BERT to aspect-level classification
tasks. The difference is that BERTABSA directly uses the hidden states of the
aspect words to classify, while Att-BERTABSA adds an attention layer to the
output of BERT. To show that our method truly improves the results, we only
use the most critical parts of the model without any other tricks or
mechanisms (e.g. the gating mechanism). We conduct experiments on three
benchmark datasets of aspect-based sentiment analysis and PBSA outperforms all
the baselines on all datasets both in accuracy and Macro-F1. As shown in Table
V, compared with other tasks, PBSA has a more significant improvement on these
small-scale datasets, indicating that the original attention lacks a good
inductive bias due to limited labeled data. With the help of PBSA, the
robustness of the model can be improved effectively.

###  IV-D RQ1: Performances under Different Sample Ratios

To verify the performance of our approach on low-resource tasks, we conduct
experiments on different values of sample ratio. We get sample sets from the
original datasets with sample ratio $\in\\{0.001,0.005,0.01,0.05,0.1\\}$, and
measure the performances of BERT and BERT+PBSA on these sample sets according
to their accuracy.

As shown in Figure 4, the performances of BERT and BERT+PBSA have the same
trend. As the accuracy of BERT increases, the accuracy of BERT+PBSA increases
and vice versa. As explained in Section III-C, the attention supervision
information is obtained based on the pre-trained model, whose performance has
a direct influence on the quality of the attention supervision information and
further affects the results of re-training. That may explain the strong
correlation between the performance of BERT and BERT+PBSA.

The improvement is more prominent when the ratio is in the middle range
(sample ratio$\in(0.005,0.05)$). As listed above, when the ratio is small, the
pre-trained model has a bad performance, which results in meaningless
attention supervision information and further limits the performance of PBSA.
As the value of the sample ratio increases, the original model performs
better, and the quality of attention supervision information is enhanced, and
then PBSA improves the model even more. However, the improvement is not
without limitation. As the value of the sample ratio exceeds a certain value,
the phenomenon of attention bias is no longer evident, and the improvement
reduces. It may be because BERT is pre-trained on a large-scale corpus, and
when we fine-tune it, its attention fits well on these ’larger-scale’ sample
sets, which makes the original model has scant room for improvement.

To sum up, the distribution of the attention parameters is not stable enough
when the data is limited or the model size is small, which can be refined by
PBSA. And the performance and lifting area of PBSA are closely related to the
performance of the baseline.

###  IV-E RQ2: Comparison with other methods

On the tasks listed above, we compare our method with other advanced self-
supervised attention learning approaches. SANA [6] generates counterfactuals
by a masking scheme and measures the difference in the softmax probability of
the model between the counterfactual and original sample as an indicator of
important words. AWAS [5] and PGAS [32] progressively mask the word with the
largest attention weight or partial gradient. Most of these works don’t
publish their critical code and do their experiment only on certain specific
tasks, so we directly compare our algorithm with their best results published
on different tasks respectively. To make a fair comparison, we use the same
word embedding and the same settings of hidden size to reproduce their
baselines, which is listed in Table II.

On the document-level and sentence-level tasks (Table IV), PBSA is superior to
SANA by 1.11% and 1.37%, which verifies that the word-based concurrent
perturbation can mine the importance distribution of words more accurately
than the masking scheme. On the aspect-level task (Table VI), compared with
AWAS and PGAS, our method improves the model more. As we mentioned in the
Introduction (Section I), our method can generate word-specific attention
supervision while others treat the important words equally without
discrimination. We speculate that this may be one of the main reasons for our
improvement.

Figure 5: The chart of the fluctuations of Macro-F1 when we change the values
of hyperparameters.

###  IV-F RQ2: Comparison with human intuition methods

From the aspect of human intuition, the gradient-based methods and leave-one-
out methods are usually used to improve the interpretability of model. The
current self-supervised attention learning methods are mostly based on word
masking, which can be seen as a variation of leave-one-out methods. We also
try to use the gradient-based method [51] to generate supervision information.
As shown in Table III and Table V, the gradient-based method does badly on
most of the datasets, especially on aspect-level datasets. These results
demonstrate that although the gradient-based method can improve the
interpretability of the model, it does not necessarily improve the
performance. However, our method enhances interpretability while also
improving its performance.

###  IV-G RQ3: Hyperparameter sensitivity

As shown in Figure 5, our method achieves the best results on REST and TWITTER
when $T=2$ and $T=1$ respectively. When the increase of $T$, the performance
increases initially and then decreases due to over-fitting. The performance of
models won’t change sharply with the increase of $T$ once they achieve the
best results. In practice, we find that one iteration has achieved promising
results. The hyperparmeter $\lambda$ controls the perturbation degree of WBCP,
when $\lambda$ is too large, it will deteriorate performance due to injecting
too much noise. In all of our experiments, we set $\lambda$ as $0.1$. The
hyperparmeter $\gamma$ controls the strength of attention supervision, when
$\gamma$ is too large, it easily leads to overly penalize the alignment
between the model attention and perturbation attention, which may hurt the
model’s internal reasoning process.

Compared with $\gamma$, $\lambda$ has less effect on results when the value of
which changes slightly, but we cannot remove
${\sum}_{i=1}^{n}\log({-\sigma_{i}})$ from our loss function. Otherwise, the
model will try not to add any noise to $x$ without the term, which makes PBSA
get a meaningless supervision distribution that varies dramatically for the
same sentence each time (the distribution is supposed to be essentially
unchanged for the same sentence). On the other hand, results are more
sensitive to $\gamma$, which determines if the models can reach the peak of
the results.

###  IV-H RQ4: Visualization analysis

In this section, we select several attention visualizations on SST2 test set
to explain how PBSA works. As shown in Figure 3, we see that PBSA makes the
model pay more attention to important but low-frequency words, reduces the
focus on high-frequency words that do not affect the results, increases the
difference in weight between words with conflicting meanings, and increases
sensitivity to adversative relations in sentences.

#### Pay more attention to important but low-frequency words

Some words do have important effects on the results, but if they do not appear
frequently enough then the traditional attention mechanism may not pay enough
attention to them. As shown in Figure 3-(1), the word drowsy has an important
influence on the emotional polarity of the film. However, it is a low-
frequency word in the corpus, which makes the attention mechanisms do not
allocate enough weights to it, resulting in a classification error. After
being trained by PBSA, the model can assign enough weights to drowsy, which
changes the result from false to correct.

#### Reduce the focus on high-frequency words that do not affect the results

In baseline, some high-frequency words which do not contain any emotional
polarity usually get high weights, while some important words that should have
been focused on are ignored. As Figure 3-(2) shows, romantic and doesn’t are
words with strong emotional polarity. However, the baseline assigns greater
weights to other high-frequency words (e.g., between) with no emotional
polarity, and thus ignores the words romantic and doesn’t which results in
misclassification. After being trained by PBSA, the model reduces the focus on
between and the weights allocated to the significant words increase
correspondingly, which turns the result.

#### Increase the difference in weight between words with conflicting meanings

As shown in Figure 3-(3), the baseline focuses on too many words: horror,
revenge, perfect, relentless, torture, and so on. Maybe all of the words are
important but the meanings of them are conflicting, which interferes with the
classification task. The model feels confused because it does not know how to
make a prediction according to so many emotional words. After being trained by
PBSA, the difference in the weight of emotional words becomes larger, which
makes it get the right result. It should be noted that the entropy of
attention distribution may not decrease because PBSA keeps attention to
important words while diluting the distribution of the other words.

#### Be more sensitive to adversative relations in sentences

If there are adversative conjunctions (e.g., but, however, and so on) in the
sentence, it is likely to express two opposite emotions before and after the
adversative conjunction. This is when the model needs to keenly feel the
changes of emotional polarity in the sentence. From this aspect, the model is
also supposed to assign higher weights to those adversative conjunctions.
Judging from our results, it is unfortunate that the original attention
mechanism tends to ignore these conjunctions for they seem to have no effect
on results outwardly. As Figure 3-(4) and Figure 3-(5) show, the baseline
ignores the word but and results in errors. After being trained by PBSA, the
baseline pays more attention to but which makes both of the emotions before
and after the adversative conjunction can be taken into consideration.

##  V Conclusions and future work

In this paper, we propose a novel self-supervised attention learning method
based on word-based concurrent perturbation. The algorithm adds as much as
noise to each word in the sentence under the premise of unchanged semantics to
mine the supervision information to guide attention learning. Our experiments
demonstrate that our method achieves significant performance improvements over
the baselines on several text classification tasks. Moreover, we use several
visualization samples to interpret how our method guides the internal
reasoning process of models.

It is worth to note that we combine our method with transformers, which is not
implemented in most of the previous attention guiding methods. Our strategies
may not be the best ways to apply our algorithm into transformers, but they
still prove the effectiveness of the proposed method. We will try to find more
appropriate and effective strategies and incorporate our algorithm into other
NLP tasks in the future.

## References

  * [1] D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” _arXiv preprint arXiv:1409.0473_ , 2014\.
  * [2] M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” in _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_ , 2015, pp. 1412–1421.
  * [3] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in _Advances in Neural Information Processing Systems_ , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30, 2017.
  * [4] Z. Lin, M. Feng, C. N. d. Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio, “A structured self-attentive sentence embedding,” _arXiv preprint arXiv:1703.03130_ , 2017.
  * [5] J. Tang, Z. Lu, J. Su, Y. Ge, L. Song, L. Sun, and J. Luo, “Progressive self-supervised attention learning for aspect-level sentiment analysis,” in _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , 2019, pp. 557–566.
  * [6] S. Choi, H. Park, J. Yeo, and S.-w. Hwang, “Less is more: Attention supervision with counterfactuals for text classification,” in _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , 2020, pp. 6695–6704.
  * [7] Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, “Hierarchical attention networks for document classification,” in _Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies_ , 2016, pp. 1480–1489.
  * [8] Q. Chen, X. Zhu, Z.-H. Ling, S. Wei, H. Jiang, and D. Inkpen, “Enhanced lstm for natural language inference,” in _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , 2017, pp. 1657–1668.
  * [9] M. Barrett, J. Bingel, N. Hollenstein, M. Rei, and A. Søgaard, “Sequence classification with human attention,” in _Proceedings of the 22nd Conference on Computational Natural Language Learning_ , 2018, pp. 302–312.
  * [10] Y. Bao, S. Chang, M. Yu, and R. Barzilay, “Deriving machine attention from human rationales,” in _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , 2018, pp. 1903–1913.
  * [11] E. Wallace, T. Zhao, S. Feng, and S. Singh, “Concealed data poisoning attacks on NLP models,” in _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_. Online: Association for Computational Linguistics, Jun. 2021, pp. 139–150.
  * [12] H. Xu, S. Li, R. Hu, S. Li, and S. Gao, “From random to supervised: A novel dropout mechanism integrated with global information,” in _Proceedings of the 22nd Conference on Computational Natural Language Learning_ , 2018, pp. 573–582.
  * [13] X. Li, L. Bing, W. Lam, and B. Shi, “Transformation networks for target-oriented sentiment classification,” in _Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , 2018, pp. 946–956.
  * [14] Y. Zhang, I. Marshall, and B. C. Wallace, “Rationale-augmented convolutional neural networks for text classification,” in _Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing_ , 2016, pp. 795–804.
  * [15] O.-M. Camburu, T. Rocktäschel, T. Lukasiewicz, and P. Blunsom, “e-snli: natural language inference with natural language explanations,” in _Proceedings of the 32nd International Conference on Neural Information Processing Systems_ , 2018, pp. 9560–9572.
  * [16] E. Sood, S. Tannert, P. Mueller, and A. Bulling, “Improving natural language processing tasks with human gaze-guided neural attention,” in _Advances in Neural Information Processing Systems_ , vol. 33, 2020, pp. 6327–6341.
  * [17] E. Sood, S. Tannert, D. Frassinelli, A. Bulling, and N. T. Vu, “Interpreting attention models with human visual attention in machine reading comprehension,” in _Proceedings of the 24th Conference on Computational Natural Language Learning_ , 2020, pp. 12–25.
  * [18] J. Malmaud, R. Levy, and Y. Berzak, “Bridging information-seeking human gaze and machine reading comprehension,” in _Proceedings of the 24th Conference on Computational Natural Language Learning_ , 2020, pp. 142–152.
  * [19] C. Sen, T. Hartvigsen, B. Yin, X. Kong, and E. Rundensteiner, “Human attention maps for text classification: Do humans and neural networks focus on the same words?” in _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , 2020, pp. 4596–4608.
  * [20] J. Li, W. Monroe, and D. Jurafsky, “Understanding neural networks through representation erasure,” _arXiv preprint arXiv:1612.08220_ , 2016.
  * [21] S. Choi, H. Park, and S.-w. Hwang, “Counterfactual attention supervision,” in _2019 IEEE International Conference on Data Mining (ICDM)_. IEEE, 2019, pp. 1006–1011.
  * [22] D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” _arXiv preprint arXiv:1610.02136_ , 2016.
  * [23] C.-H. Chang, E. Creager, A. Goldenberg, and D. Duvenaud, “Explaining image classifiers by counterfactual generation,” in _International Conference on Learning Representations_ , 2018.
  * [24] J. Yi, E. Kim, S. Kim, and S. Yoon, “Information-theoretic visual explanation for black-box classifiers,” _arXiv preprint arXiv:2009.11150_ , 2020.
  * [25] M. Wu, M. Wicker, W. Ruan, X. Huang, and M. Kwiatkowska, “A game-based approximate verification of deep neural networks with provable guarantees,” _Theoretical Computer Science_ , vol. 807, pp. 298–329, 2020.
  * [26] E. La Malfa, M. Wu, L. Laurenti, B. Wang, A. Hartshorn, and M. Kwiatkowska, “Assessing robustness of text classification through maximal safe radius computation,” in _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings_ , 2020, pp. 2949–2968.
  * [27] T.-W. Weng, H. Zhang, P.-Y. Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh, and L. Daniel, “Evaluating the robustness of neural networks: An extreme value theory approach,” in _International Conference on Learning Representations_ , 2018.
  * [28] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” _arXiv preprint arXiv:1810.04805_ , 2018.
  * [29] P. He, X. Liu, J. Gao, and W. Chen, “Deberta: Decoding-enhanced bert with disentangled attention,” _arXiv preprint arXiv:2006.03654_ , 2020.
  * [30] K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning, “Electra: Pre-training text encoders as discriminators rather than generators,” _arXiv preprint arXiv:2003.10555_ , 2020.
  * [31] D. Tang, B. Qin, and T. Liu, “Aspect level sentiment classification with deep memory network,” in _Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing_ , 2016, pp. 214–224.
  * [32] J. Su, J. Tang, H. Jiang, Z. Lu, Y. Ge, L. Song, D. Xiong, L. Sun, and J. Luo, “Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning,” _Artificial Intelligence_ , vol. 296, p. 103477, 2021.
  * [33] A. Jacovi and Y. Goldberg, “Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?” in _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , 2020, pp. 4198–4205.
  * [34] H. Kamigaito, K. Hayashi, T. Hirao, H. Takamura, M. Okumura, and M. Nagata, “Supervised attention for sequence-to-sequence constituency parsing,” in _Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)_ , 2017, pp. 7–12.
  * [35] Y. Zou, T. Gui, Q. Zhang, and X.-J. Huang, “A lexicon-based supervised attention model for neural sentiment analysis,” in _Proceedings of the 27th international conference on computational linguistics_ , 2018, pp. 868–877.
  * [36] M. Nguyen and T. H. Nguyen, “Who is killed by police: Introducing supervised attention for hierarchical lstms,” in _Proceedings of the 27th International Conference on Computational Linguistics_ , 2018, pp. 2277–2287.
  * [37] F. Zhao, Z. Wu, and X. Dai, “Attention transfer network for aspect-level sentiment classification,” in _Proceedings of the 28th International Conference on Computational Linguistics_ , 2020, pp. 811–821.
  * [38] X. Hu, X. Kong, and K. Tu, “A multi-grained self-interpretable symbolic-neural model for single/multi-labeled text classification,” _arXiv preprint arXiv:2303.02860_ , 2023.
  * [39] S. Jain and B. C. Wallace, “Attention is not explanation,” in _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_ , 2019, pp. 3543–3556.
  * [40] R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in _Proceedings of the 2013 conference on empirical methods in natural language processing_ , 2013, pp. 1631–1642.
  * [41] X. Li and D. Roth, “Learning question classifiers,” in _COLING 2002: The 19th International Conference on Computational Linguistics_ , 2002.
  * [42] B. Pang and L. Lee, “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,” _arXiv preprint cs/0506075_ , 2005.
  * [43] M. Hu and B. Liu, “Mining and summarizing customer reviews,” in _Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining_ , 2004, pp. 168–177.
  * [44] B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” _arXiv preprint cs/0409058_ , 2004.
  * [45] J. Wiebe, T. Wilson, and C. Cardie, “Annotating expressions of opinions and emotions in language,” _Language resources and evaluation_ , vol. 39, no. 2, pp. 165–210, 2005.
  * [46] A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in _Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies_ , 2011, pp. 142–150.
  * [47] M. Pontiki, H. Papageorgiou, D. Galanis, I. Androutsopoulos, J. Pavlopoulos, and S. Manandhar, “Semeval-2014 task 4: Aspect based sentiment analysis,” _SemEval 2014_ , p. 27, 2014.
  * [48] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, “Adaptive recursive neural network for target-dependent twitter sentiment classification,” in _Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers)_ , 2014, pp. 49–54.
  * [49] C. Peng, Z. Sun, L. Bing, and Y. Wei, “Recurrent attention network on memory for aspect sentiment analysis,” in _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing_ , 2017.
  * [50] S. Wang, S. Mazumder, B. Liu, M. Zhou, and Y. Chang, “Target-sensitive memory networks for aspect sentiment classification,” in _Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , 2018, pp. 957–967.
  * [51] S. Serrano and N. A. Smith, “Is attention interpretable?” in _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , 2019, pp. 2931–2951.
  * [52] P. Zhou, W. Shi, J. Tian, Z. Qi, B. Li, H. Hao, and B. Xu, “Attention-based bidirectional long short-term memory networks for relation classification,” in _Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers)_ , 2016, pp. 207–212.
  * [53] J. Dai, H. Yan, T. Sun, P. Liu, and X. Qiu, “Does syntax matter? A strong baseline for aspect-based sentiment analysis with roberta,” _CoRR_ , vol. abs/2104.04986, 2021. [Online]. Available: https://arxiv.org/abs/2104.04986
  * [54] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality arxiv : 1310 . 4546v1 [ cs . cl ] 16 oct 2013,” 2013.

##  Appendix A Analysis of the Extra Computations

The extra computations mainly come from the process of generating supervision
information (Pre-training and re-training are the same as the common training
method). The extra time required depends on the size of the model, the number
of the samples and the epochs of training perturbation model. It is acceptable
for most datasets because the whole process is parallel. All the sub-
perturbation models have independent samples and training processes, and they
just share the same pre-trained model whose parameters are fixed during the
generating process. Therefore, the whole process can be handled concurrently
if having enough GPU resources.

For SST2, TREC, MR, CR, SUBJ, and MPQA, the generating process (batch-size=64)
can be finished on 2 * GTX 3090 within less than 15 min. Some small datasets
(e.g. SST2, TREC and CR) only need 8 min to generate supervison information.
However, as for IMDB, the number of samples is enormous, and their average
length is too long. Therefore, we must use several GPUs (2 * GTX 3090 and 4 *
GTX 1080ti) to simultaneously deal with each part of the dataset to finish the
task in a limited time.

##  Appendix B Details of Baselines

The details of our baselines are listed below.

Figure 6: The illustration of Att-BiLSTM.

Figure 7: The illustration of Memory Net.

###  B-A Att-BiLSTM

Figure 7 shows the structure of Att-BiLSTM. Att-BiLSTM first map each word
into pre-trained skip-gram [54] word embedding and then utilize 1-layered
BiLSTM with a scale-dot attention mechanism to get sentence-level hidden
states which are finally used for classification.

###  B-B Memory Network

Figure 7 shows the structure of MN. Memory Network uses an iteratively updated
vector $A$ (initialized as the aspect embedding) and the context embedding to
generate the attention distribution, which is then used to select the
important information from the context embedding and iteratively update the
vector $A$.

###  B-C Att-BERT

Figure 10 shows the structure of Att-BERT. We add a scale-dot attention layer
to the output of the BERT and use the output of the attention layer to
classify.

###  B-D BERTABSA

Figure 10 shows the structure of BERTABSA. We input the whole sentence to get
the context representation of the aspect words, which is directly used for
classification. To verify that our method truly improves the results, we
delete the gating mechanism and use bert-base-uncased instead of bert-large-
uncased.

###  B-E Att-BERTABSA

Figure 10 shows the structure of Att-BERTABSA. Its structure is similar to
Att-BERT, for adding a scale-dot attention layer after the output of BERT.
However, different from Att-BERT, the hidden states of context words and
aspect words are regarded as $Q$ and $K$ respectively and fed into the
attention layer separately. To verify the effectiveness of our method, we make
the same modifications on the Att-BERTABSA.

Figure 8: The illustration of Att-BERT.

Figure 9: The illustration of BERTABSA.

Figure 10: The illustration of Att-BERTABSA.