Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction∗∗

Yidong Wang1
Hao Wu1 Ao Liu1 Wenxin Hou2†
Zhen Wu3 Jindong Wang4 Takahiro Shinozaki1 Manabu Okumura1 Yue Zhang5
1
Tokyo Institute of Technology 2 Microsoft STCA
3
Nanjing University 4 Microsoft Research Asia 5 Westlake University{yidongwang37, wu.364371691}@gmail.com, wuz@nju.edu.cn

Abstract

arXiv:2208.08280v1 [cs.CL] 17 Aug 2022

The dishes are amazingly delicious but the waiter is so rude .

Target-oriented Opinion Words Extraction(TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target
from the sentence. Recently, deep learning
approaches have made remarkable progresson this task. Nevertheless, the TOWE task
still suffers from the scarcity of training data
due to the expensive data annotation process.
Limited labeled data increase the risk of distribution shift between test data and training
data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of
unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared withcurrent state-of-the-art methods. The in-depth
analysis also demonstrates the effectivenessof the different-granularity filters. Our codes
are available at https://github.com/
TOWESSL/TOWESSL.

1Introduction

Target-oriented Opinion Words Extraction(TOWE) (Fan et al., 2019) is an important subtask
of aspect-based sentiment analysis (ABSA) (Pontiki et al., 2014), which aims to extract thecorresponding opinion words for a given opinion
target from the sentence. For the TOWE task,
opinion targets, also called aspect terms, are theentities or objects in the sentence toward whichusers show attitudes; opinion words, sometimes
known as opinion expressions, are those words
explicitly mentioned in the sentence and used to∗†Equal Contribution.
Corresponding author.

Given opinion target: dishes
Given opinion target: waiter

Corresponding opinion words: amazingly delicious
Corresponding opinion words: rude

Figure 1: Example of TOWE. Words in red are opinion targets and words in blue are corresponding opinion
words. TOWE extracts corresponding opinion words
when given opinion targets.

express attitudes or opinions. Figure 1 shows anexample of the TOWE task. For the sentence “Thedishes are amazingly delicious but the waiter is so
rude.”, the terms “dishes” and “waiter” are two
opinion targets. The goal of TOWE is to extract“amazingly delicious” as the opinion words for theopinion target “dishes” and opinion word “rude”
when given the opinion target “waiter”.
While seminal work casts TOWE as a sequencelabeling problem, using fully supervised learning
methods to identify opinion words and phrases
from sentences, recent work shows that externalsources of information can be highly useful for
improving the performance. In particular, both syntactic knowledge (Dai et al., 2022; Veyseh et al.,2020; Jiang et al., 2021; Zhang et al., 2021b) andsentiment information (Wu et al., 2020b) have been
exploited, with the former helping to identify thecorrelation between opinion targets and opinion expressions, and the latter helping to identify words
and phrases that are correlated with sentiment polarities. Existing work integrates these externalfeatures via representation structures (Dai et al.,2022; Veyseh et al., 2020; Jiang et al., 2021) andmulti-task learning (Wu et al., 2020b; Zhang et al.,2021b).
Intuitively, the goal of opinion words extraction is to obtain structured knowledge from raw
data, and therefore ideally the amount of test data,
namely the data from which opinion words aremined, should be large. This can inevitably increase the risk of distribution shift between test

instances and the training data, even if the test data
is from the same domain (Ganin et al., 2016). Existing work, however, adopt a supervised training
setting, with the model being tuned on a set of fixed
training data. To address this issue, we consider
making use of raw text inputs to increase the exposure of the model to varying distribution shifts.
Our main idea is to (1) find raw data in a similar
domain as the target-oriented opinion words extraction training data (i.e. gold data), (2) using a modeltrained on the gold data to assign silver labels, andthen (3) design a set of filters to select the mostuseful set of silver data, so that (4) the selectedsilver data can be used together with the gold data
for training a final model.
As a pre-processing step, we train a opinion target extraction model, which is used to label the setof raw data. Those raw sentences without any opinion target are filtered. The resulting raw data has
the same format as the input structure of TOWE
data. Then in our implementation, steps (2) to (4)above are done in a joint batch training process.
First, we initialize a TOWE model using BERT.
Then in each batch, we randomly sample a subsetof gold data and a subset of raw data, using thecurrent model to assign pseudo labels to the raw
data. According to the current model probabilities
and an external sentiment classifier, we filter the silver data by removing low-confidence sentences, aswell as masking low-confidence words from the remaining sentences. Finally, the model parameters
are updated by using the standard cross-entropy
losses separately on both the gold and silver data,so that the next batch of training can start with the
new model. The training process continues for a
fixed number of iterations, and the model with thehighest development scores are selected for testing.
Results on four standard benchmarks show that
our method gives significantly improved results
when raw data are used. We achieve the bestreported results on all the datasets. In addition,
the critical ablation demonstrates our sentencelevel and word-level noises filtering both bring
improvements for the final results. In-depth analysis shows our method significantly reduces theextraction errors on different error types. To ourknowledge, we are the first to consider the useof unlabeled data for TOWE, successfully giving
state-of-the-art results on benchmarks. Our codeand datasets are available at https://github.
com/TOWESSL/TOWESSL.

Labeled data:
The/O entire/O dining/O experience/O was/O very/B wonderful/I !/O
Unlabeled data with the generated pseudo opinion target:
Their menu is too expensive for a bubble drink .

Figure 2: Examples of labeled data and unlabeled datawith the pseudo opinion target. Words with underline
indicate opinion targets. The span in the labeled data
beginning with B and followed by I represent the corresponding opinion words.

2

Background

2.1

Task Formalization

The TOWE task requires the opinion target as the
input and extracts the target-oriented opinion words
from the sentence. It can be formulated as a targetoriented sequence labeling task. In this work, we
use the notation s to represent a labeled sentencefrom the TOWE dataset, and use su to denote a
unlabeled sentence. Formally, given an input sentence s = {w1 , w2 , . . . , wn } consisting of n words
and an opinion target wt in s (here we notate an
opinion target as one word for simplicity and t is
the position of the opinion target in the sentence),
the goal of TOWE is assign a corresponding label yi ∈ {B, I, O}(B: Beginning, I: Inside, O:
Others) for each word wi in s. The spans beginningwith B and followed by I represent the corresponding opinion words of the opinion target wt . Figure
2 shows an sequence labeling example of TOWE.
2.2

Pseudo Opinion Targets LabelingRaw unlabeled data can not be directly used in
the semi-supervised scenario for TOWE, as theylack the necessary annotations of opinion targets.
We use the opinion targets in labeled TOWE dataas the ground truth to train a BERT-based target
extraction model.
Specifically, given an input sentence s ={w1 , w2 , . . . , wn } from the TOWE dataset, we employ a BERT model (Devlin et al., 2019) to generate the context representation of the each word wi
as follows:pt
hpt
1 , . . . , hn = BERT(w1 , . . . , wn )(1)

Then the context representation hpt
i of the word
wi is fed to a linear layer and a softmax layer topredict the corresponding label. Similar to TOWE,
the BIO scheme is used. We train the opinion target
extraction model by minimizing the cross-entropy
loss between the predicted BIO label distribution

Sentence

BERT & Opinion
Transformer
Target Embedding

𝑤1

𝑐1

𝑤2

𝑐2

𝑤3

𝑐3

MGCR

𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑠𝑢𝑚
𝑠𝑐𝑠𝑒𝑛𝑡𝑖

Decoding
for TOWE

…

…

…

…

𝑤𝑛

Sentence-level Filtering The TOWE Model

Word-level Filtering

𝑐𝑛

𝑇

𝜏

The Pre-trained
Sentiment Classifier

𝑤1
𝑤2

𝛼1 𝛼2 𝑎3 … 𝛼𝑛

𝑤3

…

…

…

BERT

BiLSTM

Sentiment
Classification

attention

𝑤𝑛
Sentence

pooling

Figure 3: Overview of the architecture of multi-grained consistency regularization. For simplicity, we mark theconfidence of i-th word as ci . Note that the input sentence of the TOWE model is the same as the input sentenceof the pre-trained sentiment classifier.

and the ground truth. After training, we can employ
the opinion target extraction model to obtain thepseudo opinion targets of the unlabeled sentencesu , which makes semi-supervised learning feasible
for the TOWE task.

the H(·, ·) refers to the cross-entropy loss. In thiswork, we use Random Mask and Random Synonym
Replacement by using WordNet (Miller, 1995) as
the perturbing function ω.

3
2.3

MethodConsistency Regularization

Consistency regularization is a semi-supervised
learning method that shows effectiveness in a variety of fields (Sohn et al., 2020; Chen et al., 2020;
Xie et al., 2020; Zhang et al., 2021a). It improves
the generalization performance of a TOWE modelby generating a perturbed version ω(su ) of theoriginal unlabeled sentence su and forcing the predicted category of each word to be the same, whereω is the perturbing function (Lee et al., 2013).
To perform semi-supervised learning for TOWE,
we feed the unlabeled sentence su and the positiont of the pseudo opinion target wtu to the TOWE
model (see section 3.1), and obtain the predictedprobability pi (y|θ; su , t) of the word wiu , where θ
represents the parameters of the TOWE model. A
vanilla consistency loss for consistency regularization of TOWE is computed as:

3.1

n

1X
H(p̂i (y|θ; su , t), pi (y|θ; ω(su ), t)),nFigure 3 shows the the framework of our MultiGrained Consistency Regularization (MGCR)
method. The TOWE model is a BERT-based neuralsequence labeling network. As mentioned in the
introduction, we initialize a BERT TOWE model(Section 3.1), and then iteratively conduct batch
training. In each batch, we impose a standard crossentropy loss on a set of sampled gold data (Eq 5),
and a regularization loss on a set of randomly sampled raw data with targets (Eq 11). The latter isdefined by using the current model to assign TOWE
labels, and then adding a sentence-level (Section 9)
and a word-level (Section 10) filter. Besides, asmentioned in the introduction, MGCR exploits latent opinion words from a pre-trained review sentiment classification (Wu et al., 2020b) to filter
noises more accurately.

(2)

i=1

where p̂i (y|θ; su , t) = arg max pi (y|θ; su , t) andp̂i (y|θ; su , t) denotes the predicted label of the wiu ,TOWE Model

For the sentence s = {w1 , w2 , . . . , wn }, the
TOWE model first employs a pre-trained BERT togenerate the context representations {h1 , . . . , hn }of s. To incorporate the opinion target information

into the sentence representations, we use the number 1 to represent the position of opinion target wt
in s and 0 to denote the positions of other words,
and then map them to the position embedding sequence {e1 , . . . , en }. We integrate opinion targete i = [hi ; ei ].
information by concatenating, i.e., h
To better encode opinion target information, we
additionally employ a multi-layer Transformer architecture (Vaswani et al., 2017) to generate thetarget-specific context representations as follows:e1, . . . , he n ).
r1 , . . . , rn = Transformer(h(3)

the attention score αi of zi is calculated as follows:nzavg =

i=1

f (zi , zavg ) = zi · W · zavg + b,(4)

where Wr and br are learnable weight and bias.
The loss of supervised learning on the TOWE
dataset is defined as:

where W and b are learnable weight and bias.
We then compute the sentiment-aware confidence
based on the obtained attention scores as:
scsenti =

(5)

i=1

3.2

Multi-Grained Consistency
Regularization1(scsenti > T ),

(6)

i=1

where max(pi (y|θ; su , t)) (i.e., the maximum of
the probabilities of B, I, and O) is the confidenceof the i-th word. Sentences with confidences below the given threshold T are masked during training. In Eq. (6), different words in the sentence aretreated equally, which ignores the importance ofthe opinion words. We highlight the confidencesof opinion words identified by their larger sentiment scores. Specifically, we obtain the sentimentattention scores from a pre-trained attention-based
sentiment classifier parameterized by θsenti from
(Wu et al., 2020b)1 . After acquiring the representations {z1 , z2 , . . . , zn } of the unlabeled sentence,
1

The sentiment classifier we use is the same as (Wu et al.,2020b), except that we use BERT as the word representation
model while they use GloVe vectors (Pennington et al., 2014).

(8)

(9)

where 1(· > T ) is the indicator function for confidence thresholding with T being the threshold.
Word-level Filtering

To further reduce the noise in the unlabeled data,
we filter the noisy words with a more fine-grained
confidence thresholding mechanism. The filteringof noisy words can be expressed as

1(max(pi (y|θ; su , t)) > τ ).

n

1X
scavg =
max(pi (y|θ; su , t)),nαi · max(pi (y|θ; su , t)).

Similar to Eq. (6), any sentence whose sentimentaware confidence is lower than the threshold T
will be masked in consistency regularization. The
final filtering mechanism of noisy sentences can be
expressed as:

3.3

3.2.1 Sentence-level Filtering
Given an unlabeled input sentence su ={w1u , w2u , . . . , wnu } with wtu as the pseudo opinion
target position, the sentence-level confidence scavg
is defined as follows:

n
X
i=1

n

1X
Ls =
H(yi , pi (y|θ; s, t)).
n(7)

ef (zi ,zavg ),αi = Pnf (zj ,zavg )j=1 eFinally, we use the representation ri to predict
the opinion word probability pi (y|θ; s, t) of theword wi when given the opinion target wt :pi (y|θ; s, t) = softmax(Wr ri + br ),

1X
zi ,
n

(10)

Any word with a confidence lower than τ ismasked. Note that the word-level threshold τ canbe different from the sentence-level threshold T .
3.4

Training Objective

For labeled sentences, the supervised loss is thesame as Eq. (5). For unlabeled sentences, the consistency loss is:
Lc =1(scsenti > T )n1X·{
1(max(pi (y|θ; su , t)) > τ )ni=1· H(p̂i (y|θ; su , t), pi (y|θ; ω(su ), t))}.
(11)
The final training objective is given by:
L = Ls + Lc .

(12)

Datasets#sentences#opinion targets

14res

Train
Test

1,627
500

2,643
864

15res

Train
Test

754
325

1,076
436

16res

Train
Test

1,079
329

1,512
457

14lap

Train
Test

1,158
343

1,634
482

Yelp

Unlabeled

100,000-

AmazonUnlabeled

100,000-

Table 1: Statistics of TOWE datasets and unlabeleddatasets. For TOWE datasets, sentence may containmultiple opinion targets. For unlabeled datasets, we
randomly sampled data from Yelp for 14res, 15res,
16res datasets and Amazon for 14lap dataset. The unlabeled data is available at https://github.com/
TOWESSL/TOWESSL.

4

Experiments

4.1

Datasets and Metrics

Following previous studies (Fan et al., 2019; Wu
et al., 2020b; Veyseh et al., 2020; Mao et al., 2021;
Jiang et al., 2021; Feng et al., 2021; Zhang et al.,2021b), we conduct evaluations on four benchmark
datasets for TOWE. The suffixes ‘res’ and ‘lap’
refer to restaurant reviews and laptop reviews, respectively. For the 14res, 15res, and 16res datasets,
we use the unlabeled sentences from Yelp2 . For
the 14lap dataset, we use the unlabeled sentencesfrom Amazon Electronics3 (Ni et al., 2019). The
statistics of these datasets are listed in Table 1.
We use the evaluation metrics of precision, recall, and F1 score to measure the performance ofdifferent methods following previous studies (Fanet al., 2019; Wu et al., 2020b; Jiang et al., 2021).
An extraction is considered correct only if opinion words from the beginning to the end are all
correctly predicted.
4.2

Experimental Settings

For our MGCR method, we set the hidden size ofboth the BERT and the Transformer to 512. The
mini-batch sizes of labeled and unlabeled data are
set to 16 and 96, respectively. All parameters are
optimized using the AdamW optimizer (Loshchilov
and Hutter, 2018) with an initial learning rate 2e-5
for BERT and 2e-4 for others. We randomly split
23

https://www.yelp.com
https://www.amazon.com

Hyperparameter
Batch size
EpochsSteps
Learning rate (BERT)
Learning rate (Others)
Hidden dimension
Optimizer

TOWE model

Sentiment Classifier16(96)
50
2e-5
2e-4
512
AdamW

128
3000
1e-5
1e-4
512
AdamW

Table 2: Experimental setting of the training of the
TOWE model and the sentiment classifier. For the
TOWE model, batch size for labeled data is 16 and 96
for unlabeled data.

20% of the training set as the validation set andused early stopping. We search different combinations of sentence-level and word-level confidence
thresholds for each dataset and use the ones withbest validation performances. The experimental
results of different threshold combinations are provided in Table 5, Table 6, Table 7 and Table 8. The
training details of the pseudo opinion targets generator and the sentiment classifier are provided inTable 2, respectively. We pre-train the sentimentclassifier on the same data as the unlabeled data
used for MGCR.
4.3

Baselines

We compare our MGCR method with the following
methods.
Distance-rule (Fan et al., 2019) uses POS tags
and regards the nearest adjective to the opinion
target as the corresponding opinion word.
Dependency-rule (Fan et al., 2019) builds templates from the training set with the POS tagsof opinion targets and opinion words and the dependency path between them, then uses the hignfrequency dependency templates for target-oriented
opinion words extraction on the testing set.
TC-BiLSTM (Fan et al., 2019) follows the design of the work for target-oriented sentiment classification (Tang et al., 2016) and concatenate an
opinion target embedding for each word position
to perform sequence labeling.
IOG (Fan et al., 2019) employs six different positional and directional LSTMs to encode sentence
and then extract the opinion words of the target.
LOTN (Wu et al., 2020b) transfers the latent
opinion knowledge from sentiment classification
task into the TOWE task via an auxiliary learningtask.
ONG (Veyseh et al., 2020) leverages syntaxbased opinion possibility scores and the syntactic

P

14res
R

F1

P

15res
R

F1

P

16res
R

F1

P

14lap
R

F1

Distance-rule (Fan et al., 2019)
Dependency-rule (Fan et al., 2019)
TC-BiLSTM (Fan et al., 2019)
IOG (Fan et al., 2019)
LOTN (Wu et al., 2020b)
ONG (Veyseh et al., 2020)
Dual-MRC (Mao et al., 2021)
PER (Dai et al., 2022)
ARGCN (Jiang et al., 2021)
TSMSA (Feng et al., 2021)
MRC-MVT (Zhang et al., 2021b)

58.39
64.57
67.65
82.85
84.00
83.23
89.79
86.43
87.32
86.31

43.59
52.72
67.67
77.38
80.52
81.46
78.43
80.39
83.59
89.42

49.92
58.04
67.61
80.02
82.21
82.33
83.73
83.30
85.42
86.37
87.83

54.12
65.49
66.06
73.24
76.61
76.63
77.19
81.50
78.81
82.04

39.96
48.88
60.16
69.63
70.29
81.14
71.98
75.05
77.69
81.54

45.97
55.98
62.94
71.35
73.29
78.81
74.50
78.14
78.24
81.64
81.79

61.90
76.03
73.46
76.06
86.57
87.72
86.07
90.00
88.49
90.60

44.57
56.19
72.88
70.71
80.89
84.38
80.77
84.00
84.95
88.19

51.83
64.62
73.10
73.25
83.62
86.01
83.33
86.90
86.69
89.20
89.38

50.13
45.09
62.45
85.25
77.08
73.87
78.21
80.68
75.83
79.59

33.86
31.57
60.14
78.51
67.62
77.78
81.66
70.72
76.90
81.12

40.42
37.14
61.21
81.69
72.02
75.77
79.90
75.38
76.36
82.18
80.84

MGCR (ours)

88.65

89.36

89.01†

84.29

83.37

83.80†

91.31

91.74

91.51†

83.76

81.25

82.47†

Methods

Table 3: Main results (%) including recall, precision and F1-score. The best results are in bold and second-best
results are underlined. Results of all comparison methods were copied from the original papers. The marker †represents that MGCR outperforms other methods significantly (p < 0.01) .
Methods
MGCR
w/o Pre-trained Sentiment Classifier
w/o Filtering Noisy Unlabeled Sentences
w/o Filtering Noisy Unlabeled Words
w/o Consistency Regularization (Labeled Data Only)

P

14res
R

F1

P

15res
R

F1

P

16res
R

F1

P

14lap
R

F1

88.65
87.69
88.84
87.29
87.34

89.36
89.03
88.00
88.12
87.05

89.01
88.35
88.41
87.70
87.19

84.29
82.79
80.13
80.10
82.42

83.37
82.89
85.39
85.33
81.81

83.80
82.77
82.62
82.66
82.11

91.31
90.67
89.59
91.02
87.19

91.74
90.60
91.68
91.30
88.38

91.51
90.63
90.62
91.16
87.76

83.76
84.18
82.84
81.99
81.70

81.25
79.19
78.83
80.19
77.89

82.47
81.05
80.77
81.07
79.70

Table 4: Ablation study results (%) when removing different components from MGCR method.

connections between the words for TOWE.
Dual-MRC (Mao et al., 2021) used BERT as
the encoder and transforms the TOWE task into aquestion answering (QA) problem to solve.
PER (Dai et al., 2022) proposes a paddingenhanced reinforcement learning model on bothsequential structure and syntactic structure to extract opinion words for opinion targets.
ARGCN (Jiang et al., 2021) proposes a directedsyntactic dependency graph and a attention-based
relational graph convolutional neural network to
exploit syntactic information for TOWE.
TSMSA (Feng et al., 2021) design a targetspecified sequence labeling with multi-head selfattention based on transformer architecture.
MRC-MVT (Zhang et al., 2021b) leverages amachine reading comprehension model trainedwith a multiview paradigm to extract targetoriented opinion words.
4.4

Main Results and Discussion

Table 3 shows main results of different methods onfour benchmarks. These results demonstrate that
MGCR achieves the best F1-score on all datasets.
Concretely, MGCR has the following advantages:• MGCR significantly outperforms other methods. In these methods, rule-based methods(Distance-rule and Dependency-rule) achieve
the worst performance, since they lack robustness and only cover a small number ofcases. By contrast, neural methods obtainobvious improvements by introducing the external knowledge (e.g., LOTN and ARGCN)
or other solution paradigm (e.g., Dual-MRC
and MRC-MVT). Among the neural methods, MRC-MVT achieves very competitive
results on all datasets with using machinereading framework. Nevertheless, our MGCR
still outperforms it by 1.18%, 2.01%, 2.13%,
and 1.63% in F1-score respectively on 14res,
15res, 16res and 14lap datasets. These comparisons demonstrate the great superiority of
MGCR in exploiting unlabeled data to reduce
distribution shift for TOWE, thereby successfully boosting the extraction performance.
• With few labeled TOWE data, MGCR canalso bring great performance gain. As illustrated in Table 1, the labeled training sentences of the 14res dataset is about twice theamount of the 15res dataset, while MGCR
outperforms MRC-MVT by 1.18% and 2.01%
in F1-score respectively on 14res and 15res
datasets. The more improvement on 15resproves that MGCR is promising because it

0.5
0.7
0.9

P

0.5
R

F1

P

0.7
R

F1

P

0.9
R

F1

89.55
89.58
88.64

86.53
87.63
87.83

88.02
88.60
88.23

88.60
87.80
87.65

88.28
88.77
89.28

88.44
88.28
88.45

87.88
88.65
87.73

89.29
89.36
88.35

88.55
89.01
88.03

91

Table 5: Results (%) of combinations of sentence-level
threshold and word-level threshold on 14res. T andτ represent sentence-level threshold and word-level
threshold respectively.
T
τ
0.5
0.7
0.9

P

0.5
R

F1

P

0.7
R

F1

P

0.9
R

F1

81.31
80.13
81.38

82.62
85.39
83.43

81.94
82.62
82.38

81.17
81.84
81.41

83.57
84.04
84.38

82.33
82.92
82.81

82.16
84.29
81.35

84.17
83.37
84.24

83.12
83.80
82.73

perform well even with few labeled sentences.
Ablation Study

To evaluate the effectiveness of each component of
MGCR, we conduct an ablation study. As shown in
Table 4, we observe different levels of performancedegradation on the four TOWE datasets when removing the components from MGCR. Specifically,
when removing the pre-trained sentiment classifier,the F1 score of MGCR drops from 0.5% to 1.5%
on all datasets, indicating that using sentimentattention scores to emphasize latent opinion words
helps better filter the noisy unlabeled sentences.
After removing either of the coarse-grained andfine-grained filtering processes, the performance
declines on all datasets, demonstrating that thesetwo confidence-based thresholding mechanisms alleviate the issue of confirmation bias caused bynoisy training signals in consistency regularization.
In addition, we find that removing the consistency
regularization (i.e., supervised learning only) worsens the performance most.
4.6

87

15res

85

16res

83

14lap

81
79

0 10000

50000

80000 100000

Amounts of Unlabeled Data

Table 6: Results (%) of combinations of sentence-level
threshold and word-level threshold on 15res. T andτ represent sentence-level threshold and word-level
threshold respectively.

4.5

14res

89

F1 Score (%)

TτResults of Different Thresholds
Combinations

We present the results of different combinationsof sentence-level threshold and word-level thresholds on four TOWE datasets. The range of eachthreshold is from 0.5 to 0.9. The detailed results
for the 14res, 15res, 16res, and 14lap dataset arelisted in Tables 5, 6, 7 and 8, respectively. An interesting finding is that using a high T with a low τis the best strategy on most datasets, which means

Figure 4: F1-score (%) on four TOWE datasets withvarying amounts of unlabeled data.
T
τ
0.5
0.7
0.9

P

0.5
R

F1

P

0.7
R

F1

P

0.9
R

F1

90.44
90.21
89.45

90.66
90.72
90.35

90.55
90.46
89.88

91.31
89.85
90.16

91.74
91.68
91.36

91.51
90.76
90.75

91.09
90.73
90.90

90.79
90.72
91.36

90.94
90.72
91.13

Table 7: Results (%) of combinations of sentence-level
threshold and word-level threshold on 16res. T and τ
represent the sentence-level threshold and word-level
threshold respectively.
T
τ
0.5
0.7
0.9

P

0.5
R

F1

P

0.7
R

F1

P

0.9
R

F1

83.29
83.78
82.43

79.65
79.48
78.83

81.42
81.56
80.77

84.85
84.54
84.21

78.07
79.36
78.82

81.32
81.86
81.36

84.43
83.29
83.76

77.51
78.48
81.25

80.82
80.78
82.47

Table 8: Results (%) of combinations of sentence-level
threshold and word-level threshold on 14lap. T and τ
represent the sentence-level threshold and word-level
threshold respectively.

we should filter noisy unlabeled sentences morestrictly than filtering noisy unlabeled words.
4.7

Effect of Amounts of Unlabeled Data

We conduct experiments by varying the amounts
of unlabeled data to investigate the effect of different data amounts. The results are shown in Figure
4. Compared with supervised training only, even a
few unlabeled data can improve the performance on
all datasets, which validates the effectiveness of utilizing semi-supervised consistency regularization.
Besides, the generalization performance is betterwith more unlabeled data used, because more data
effectively reduce the risk of domain shift between
test data and training data. This trend demonstrates
the potential of MGCR, which are likely to be further improved with even more unlabeled sentences
and computing resources.

Methods

NULL

Under-extractedOver-extracted

Others

Total

2
3
5
4
8

9
12
9
14
11

24
29
29
38
44

8
11
7
11
15

43
54
55
67
70

MGCR
MGCR w/o Pre-trained Sentiment Classifier
MGCR w/o Filtering Noisy Unlabeled Sentences
MGCR w/o Filtering Noisy Unlabeled Words
MGCR w/o Consistency Regularization (Labeled Data Only)

Table 9: Statistics of different error types of our MGCR method and different ablation versions on the 16res dataset.

4.8

Error Analysis

We count all the error instances on the 16res dataset
to analyze the distribution of different error types.
We categorize errors into four types: ‘NULL’: noopinion word is extracted, ‘Under-extracted’: only
parts of opinion words are extracted from the input sentences, ‘Over-extracted’: the model extracts
redundant opinion words, and ‘Others’: wrong extraction and other error types. As shown in Table
9, training with only labeled data suffers from thesevere overfitting problem, which leads to many‘Over-extracted’ errors. For instance, in the sentence “open & cool place with the best pizza andcoffee.”, the overfitted TOWE model extracts both“cool” and “best” as opinion words for the opinion target “coffee”, actually the word “cool” is theopinion word for “place”. Our MGCR reduces thenumber of ‘Over-extracted’ errors from 44 to 24,
indicating that introducing high-quality unlabeleddata improves the generalization performance of
the TOWE model. It is worth noting that MGCR
also reduces 40% of total errors compared withtraining with only labeled data.

5

Related Work

5.1

TOWE

Aspect-based Sentiment Analysis (ABSA) contains
a set of various subtasks (Pontiki et al., 2014; Liuet al., 2015; Tang et al., 2016; Wang et al., 2016;
Ning et al., 2018; Zhao et al., 2020; Wu et al.,2020a). Fan et al. (2019) first propose TOWE as a
new subtask to expand the ABSA research, whichaims to extract opinion words for a given opinion target from a sentence. They employ several
LSTM networks and propose a target-fused neuralsequence labeling model that achieves promising
results on TOWE. Following the idea, many works
design advanced multi-head self-attention or multiview deep attention mechanism to generate targetspecific context representation (Feng et al., 2021;
Zhang et al., 2021b). On this basis, some works
incorporate syntactic knowledge (Feng et al., 2021;

Zhang et al., 2021b) or sentiment knowledge (Wuet al., 2020b) to further improve the performanceof TOWE. There are also some other works thattransform TOWE into a question answering problem and adopt the framework of machine reading
comprehension (MRC) to solve TOWE (Mao et al.,2021; Zhang et al., 2021b), achieving very competitive results. Different from these works, we argue
that insufficient labeled data greatly increase therisk of distribution shift for the TOWE task, andthus propose making use of massive unlabeled raw
text to reduce the shift risk.
5.2

Semi-supervised Learning

Low-resource learning including semi-supervised
learning (Sohn et al., 2020; Zhang et al., 2021a;
Xie et al., 2020; Wang et al., 2022; Wang et al.;Oliver et al., 2018) has proven effective in the natural language processing (Peters et al., 2017; Heet al.; Cheng et al., 2019; Gururangan et al., 2019;
Izmailov et al., 2020; Sintayehu and Lehal, 2021;
Clark et al., 2018; Ruder and Plank, 2018; Liu et al.,2022; Lu et al., 2022; Yang et al., 2021), especially
ABSA tasks (Marcacini et al., 2018; Xu and Tan,
2019; Augenstein et al., 2018; Cheng et al., 2019;
Li et al., 2020). Consistency regularization (Sajjadi et al., 2016) is a very popular semi-supervised
learning technique, which has been widely appliedin various tasks (Berthelot et al., 2019b,a; Sohn
et al., 2020; Xie et al., 2020; Chen et al., 2020). Its
core idea is to improve the robustness of the modelby minimizing the discrepancy between data andits perturbation. Miao et al. (2020) first appliedconsistency regularization to aspect sentiment classification task by interpolating embeddings of input
sentences. By contrast, we are the first to successfully apply semi-supervised learning consistency
regularization to TOWE.

6Conclusion

The TOWE task suffers from the risk of distribution shift which arises from scarce labeled data. Inthis paper, we propose the novel MGCR method
to increase the exposure of the model to varying
distribution shifts by exploiting unlabeled data, andnaturally the risk can be reduced. In the MGCR
method, two different-grained filters, i.e., sentencelevel (coarse-grained) and word-level (fine-grained)
confidence-based thresholding, are designed tofilter noisy sentences and words for the highquality exposure. To further underline the possible opinion words during learning, we employ
a pre-trained sentiment classifier and incorporate
sentiment-attention scores into the sentence-levelfilter. Experimental results indicate that our MGCR
method significantly outperforms all other TOWE
methods and achieves state-of-the-art performanceon four TOWE datasets. The in-depth analysis
demonstrate the effectiveness of each component
in MGCR.

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