Aspect-to-Scope Oriented Multi-view Contrastive Learning  for Aspect-based Sentiment Analysis
Authors: Heyan Chai
Ziyi Yao
Siyu Tang
Ye Wang
Liqiang Nie
Binxing Fang
Qing Liao
Venue: EMNLP 2023 Findings (2023)
Keywords: Graph contrastive learning
aspect-based sentiment analysis

Abstract: Aspect-based sentiment analysis (ABSA) aims to align aspects and corresponding sentiment expressions, so as to identify the sentiment polarities of specific aspects. Most existing ABSA methods focus on mining syntactic or semantic information, which still suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence. To address these issues, in this paper, we revisit ABSA from a novel perspective by proposing a novel scope-assisted multi-view graph contrastive learning framework. It not only mitigates noisy interference for better locating aspect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between sentiment polarities and syntactic/semantic information. Extensive experiments on five benchmark datasets show that our proposed approach substantially outperforms state-of-the-art methods and verifies the effectiveness and robustness of our model.

Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis
Heyan Chai Ziyi Yao', Siyu Tang', Ye Wang', Liqiang Nie
Binxing Fang', and Qing Liao 13+
Harbin Institute of Technology. Shenzhen, China
2 National University of Defense Technology, China
Peng Cheng Laboratory. Shenzhen, China
(chaiheyan, tangsiyu999, yaolll yaoziyi jestu. wiw. edu. cn,
ye. wangenudt. edu. cn, www.iniiiiii. nieliqiang@hi edu. cn, fangbxêcae cn, 1i hi,,liioinn@hiee www.hii..dd.....
Abstract
slowe people friendy
Service slow people friendyy
Aspect-based sentiment analysis (ABSA) aims align aspects and corresponding sentiment expressions, so to identify the sentiment polarities of specific aspects. Most exist- ing ABSA methods focus on mining syntac- tic semantic information, which still suffers from noisy interference introduced by the at- tention mechanism and dependency tree when multiple aspects exist in a sentence. To ad- dress these issues, in this paper, we revisit ABSA from novel perspective by proposing a novel scope-assisted multi-view graph con- trastive learning framework. not only miti- gates noisy interference for better locating as- pect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between senti- ment polarities and syntactic/semantic infor- mation. Extensive experiments on six bench- mark datasets show that our proposed ap- proach substantially outperforms state-of-the- art methods and verifies the effectiveness and robustness of our model.
(o)
Semantic relations of "people"
(b) Dependeney tree
'seevice scope
people scope
Service
people
slow
Push
were
Friendlyy
(c) Scope-assisted Contrustive I Learning (Ouns)
Figure I: Examples of input sentence "Service was slow but people were friendly"" (a) semantic relations of aspect "people" derived by attention mechanism. (b) the dependency tree of input sentence. (c) Our scope- assisted contrastive learning method.
Recent studies on ABSA task either utilize var- ious attention mechanisms to model the seman- tic relationships between aspect and its context words to obtain aspect-related sentiment represen- tation (Chen et al., 2017: Tan et al., 2019; Oh et al.. 2021; Li et al., 2021; Pang et al., 2021), or leverage syntactic information derived from syntax depen- dency tree model syntuctic structures to capture the structural dependencies between aspect and its sentiment expressions (Sun al., 2019; Wang al., 2020; Chen et al., 2020; Zhou et al., 2021).
1 Introduction
Despite remarkable progress made by the exist- ing methods, they suffer from the interference of irrelevant context words when modeling the rela- tionship between aspect words and the correspond- ing sentiment clues. On the hand, attention- based methods may improperly assign high atten- tion scores to the words irrelevant to the anchor aspect term. As shown in Figure (a), for the an- chor aspect "people", its attention score with the corresponding opinion word "friendly" (0.20) is only slightly higher than the word "slow" w.r.t. as- pect "service" (0. 8), and much lower than that with its neighbor words (c.g. 0.24 for "were"). On the other hand, the dependency tree structure in the syntactic-based methods may also introduce noisy signals, because of the interference among multiple aspects and their corresponding opinions. As shown in Figure (b), it is easy to observe that
Aspect-based sentiment analysis (ABSA) is fine- grained sentiment classification task, aiming at identifying the sentiment polarities (i.e., positive, given
neutral, or negative) of specific aspects sentence (Wang et al., 2016; Tang et al., 2022; Chen et al., 2022). Different from document- or sentence- level sentiment analysis, different aspects in a sen- tence may express different sentiment polarities, For example, given sentence "Service was slow but people were friendly"" the sentiment labels of aspect "service" and "people" are negative and pos- itive, respectively. The main challenge of ABSA is how to effectively capture the relationship between aspect term and its corresponding opinion expres- sions while preventing interference from irrelevant context words.
Corresponding Author

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the dependency tree builds an unexpected relation between "slow" and "friendly" (see the are with red color), which are opinion words of different aspects ("service" and "people"). In other words, existing methods are insufficient to accurately capture the relationship between aspect and its corresponding opinion words, discouraging the learning of aspect- dependent sentiment representations.
corresponding opinion words.
A novel multi-view graph contrastive learn- ing framework is proposed to integrate syn- tactic dependency and semantic correlation information, facilitating the learning of aspect- dependent sentiment-awand representation.
Extensive experiments on six public bench- mark datasets show the effectiveness and bustness of our proposed A2SMvCL.
To address the above issues, we separate dif- ferent aspect-related context words to prevent the noisy interference from other aspects. Specifi- cally, inspired by the constituent tree-based struc- ture (Xu et al., 2022), we try outline struc- tural text region related specific target aspect, named aspect-specific scope, and ignore all other connections involving the target aspect in the re- maining part of the sentence, as shown in Fig- ure (c). The aspect-specific scope is expected to cover all the corresponding opinion words and ex- clude the irrelevant opinion words of other aspects. Based on this idea, propose novel aspect-to- scope multi-view contrastive learning (A2SMvCL) framework to preferably facilitate the capture of the crucial aspect-dependent sentiment clues to im- prove the performance of ABSA. More concretely, we first construct two graphs, namely DepG and SemG, to provide syntactic-strucctree and semantic- correlation from different graph views. Then, scope-assisted multi-view (intra- and inter-view) graph contrastive learning module proposed to enhance the model learn the correlations and dif- ferences among aspect-related and aspect-unrelated sentiment representation derived by both views. The scope-based intra-view contrastive learning aims to align each aspect and its corresponding opinion words while separating different aspect- related words (scope) at the same time. The scope- based inter-view contrastive learning aims at fur- ther obtaining a richer aspect-dependent sentiment representation by incorporating different types of information from two graph views. Finally, an adaptive multi-graph fusion module is proposed to fuse syntax dependency and semantic information for capturing the syntax- and semantic- enhanced sentiment representation. Our main contributions are summarized follows:
2 Methodology
In this section, we describe our proposed A2SMvCL framework for ABSA in detail. As shown in Figure 2, the proposed A2SMvCL frame- work mainly contains five components: 1) Aspect- specific scope Delection, which detects the scope of the given aspect based on word vectors in a sentence; 2) Multiple Graphs Generation, which constructs and encodes two types of graph views for each sentence; 3) Scope-Assisted Multi- View Graph Contrastive Learning, which encodes differ- ent types of information while filtering out noisy signals from other aspect-specific scopes based on syntactic and semantic graphs. 4) Adaptive Multi- Graph Fusion, which fuses syntactic and semantic information, and outputs the final representations; 5) Aspect-oriented Sentiment Classification, which classifies the sentiments of given aspect.
2.1 Task Definition
Given sentence consists of words [Wi,-.. Wt41, ..., Wt+ms Wn] and its correspond- ing aspect a [w++1, W]+m], aspect-based sentiment classification aims identify the sen- timent polarity (i.e. Positive, Negative, or Neu- tral) of this aspect, where Wi is the i-th word. Moreover, for each aspect in sentence, we de- sign an auxiliary aspect-specific scope detection task to identify sequence of aspect-related terms ya [yf ..., yfl. where of (B, O} anno- tated by Xu et al. (2022) denotes the beginning of. inside of, and outside of aspect-related words.
2.2 Text Encoder
We adopt pre-trained language model BERT (De- vlin et al., 2019) encoder to encode both sentence and its corresponding aspect a. We follow previ- ous works construct BERT-based sentence- aspect pair sequence "[CT.S]s[SEP|j(SSEP)" and feed the sequence into BERT to ac- quire the contextualized representation H
The ABSA task is approached from a new perspective that devises the aspect-specific scope to separate different aspect-related con- text words, so as to preferably climinate the noisy interference from different aspect-

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Sef-Aruction
Graph
SEEANCER peslos
Seniinett Clasclicsioo Mudile
Mitaviie regain
IEzIsin ingain
Arche
Sentinent Cassifeer
Lunguoge (BERT)
Wonklorel Claoificr
(CLS) service was (SEP) service (SEP)
B
U
490008 0
slow frinndy
Aspect-peeiific Scope Detection
Mulli-gaph
Googh
OutSere
servier people were Bierdy
Scnpe-Assisted Multi-view Grapls Cl.
Appocrode
ladape
Dependency Cruph (Dept)
Figure 2: The architecture of the proposed A2SMvCI. framework.
[h1 hn Ruxdm defined by H
Syntax-based Graph Construction. For an in- put sentence s, obtain the corresponding de- pendency tree T over s through syntactical de- pendency parser'. We subsequently build an undirected graph Gdep (dubbed DepG) by taking each word in as a node and representing head- dependent relations in T as edges. Formally, the adjacent matrix A dop E R is defined by
(PPP where h; Rdm. is the hidden representation of the i-th word.
2.3 Aspect-specific Scope Detection
To accurately identify the aspect-related context words, we introduce a constituent tree-based struc- ture, aspect-specific scope, to separate aspect- related and aspect-unrelated words. Aspect-specifie scope is the smallest continuous sub-region of the input sentence, containing an aspect and the corre- sponding sentiment expression. Specifically, given word vector matrix learned by the Text Encoder, we utilize linear-chain conditional random fields (CRF) layer (Lafferty et al., 2001) to predict the uspect-specific scope, which can be formulated as: (1)
1 if link(i,,)) = or = otherwise.
A dep
(3)
where link(i,j) means i-th and j-th words have dependency relation. To encode the syntactic infor- mation of dde,, we employ GCN module (named DepGCN) capture richer syntactic graph repre- sentation Hdep,
H dep (ALLUAA dep H, dop WW + b((u)
(4)
y = CRF(H)
where y = [M,,., v.l, and of E (B,1,0). Note that, vff = B or T denotes the i-th word of sentence is in aspect-specific scope, and y/ O indi- cates the i-th word is in aspect-specific scope. The objective of aspect-specific scope detection is minimizing the negative log-likelihood (NLL) loss:
where Whep E Rdm Xdm and b E Rdm are model parameters for the I-th layer. The initial input Hd of first GCN is derived by the node representations H learned by Text Encoder in Sec- tion 2.2, i.c Hor H [hi. ...,... hn).
Semantic- based Graph Construction. Addi- tionally, to capture the semantic correlations among words in a sentence, we build a directed graph Geem (dubbed SemG) by taking each word as a node and the similarity scores between two ar- bitrary words computed by self-attention mech- anism edges. Formally, the adjacent matrix Asem Baxn is given by
Escope
logp(Y" -oooop
2.4
Multiple Graph Generation
To leverage the syntactic structure and semantic cor- relation information of the input sentence, we de- sign syntax graph construction and semantic graph construction modules. We feed the generated De- pendency Graph (DepG) containing syntactic struc- ture information obtained by dependency tree and Semantic Graph (SemG) containing rich semantic correlation information obtained by self-attention mechanism into two GCN encoders for acquiring aspect-dependent graph representations.
(QW,)
A sem softmax
(5)
Vdm
where Q and K are two copies of H. representing query and key vectors, respectively. WR, W E
We use spaCy toolkit: https://spacy.io

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Rdmxdm are model parameters. dm is the dimen- sionality of node feature. Similar to DepG, we also employ a SemGCN module (same structure with DepGCN but does not share parameters) to capture richer semantic representation H of SemG.
the cosine similarity and (()) is nonlinear projec- tion to enhance the expression power of the critic. Similarly, we calculate C for the DepG view. Finally, by combining the two losses, we have the intra-view contrastive learning objective function in each mini-batch B, which can be defined as:
2.5 Contrastive Learning
Scope-Assisted Multi- Graph
1
Lintra N8
Σ (Chntro (2:)) + Cittra (u:)) (7)
Based on the generated DepG and SemG learned over syntax dependency tree and semantic correla- tion information, here we discuss how to effectively leverage these two types of information to capture richer aspect-dependent sentiment representation. To mitigate the noise like irrelevant relations and irrational attention scores brought up by DepG and SemG, we introduce aspect-specific scope, and de- sign novel scope-assisted multi-view graph con- trastive learning to align each aspect and its corre- sponding opinion words while separating in- and out-scope words at the same time. Specifically, intra-view contrastive learning is conducted within each graph, and inter-view contrastive learning is processed between two graphs.
2.5.2 Inter-View Contrastive Learning
To further enhance the sentiment representation of aspect-related embedding, we perform contrastive learning between two graph views. This takes the correlation among different types of graph views into consideration while preferentially performing the contrastive learning learning of aspect-related word representation.
More concretely, taking the SemG view an ample, we define the aspect node representation os as the anchor, its corresponding representation u; and aspect-specific scope S generated in another view are trcated as positive samples. Naturally, we define all remaining nodes in another view as negative samples. Moreover, to integrate the im- portance between different positive sample pairs into contrastiveness, we use the similarity of two views to re-weight positive sample pairs to improve the diversity of positive samples. The contrastive loss for SemG view computed as follow:
2.5.1 Intra-View Contrastive Learning
To better align each aspect and its corresponding opinion words while separating different aspect- related words (scope) at the same time, define intra-view contrastive loss the node repre- sentations generated by DepG and SemG views based on aspect-specific scope. For ease of dis- tinction, we denote node representations DepG and SemG views as [u.)iej)) and [ei]iefnl re- spectively. We denote aspect-specific scope in DepG and SemG views as S and S respectively. Taking the SemG view in Figure 2 an exam- ple, we define the aspect node representation v, the anchor. For any node ty in aspect-specific scope S (uy E S"), its representation vj forms the positive sample, and any node 0g not in S (uk & S") is naturally regarded as negative sam- ple. Then the contrastive loss is computed across all positive paris, (v,, 0ÿ) in a sentence by mir- roring the InfoNCE (van den Oord et al., 2018) objective,
plolermdl+ +Ejssuys e Pl
((v,, u. u.)= =-o)=-loo"" log(
D ep(u,,u)//
(8)
We define the similarity of the two views w sim (1, u:), where v, and u, are aspect node rep- resentations learned by SemG and DepG, respec- tively.
Since the two views are symmetric, the loss of the DepG view L(u,, v;) is similarly defined l(v,, u.). The overall inter-view contrastive ob- jective in each mini-batch B is formally given as:
Linter 2NB
Σ (((v; u:) +.)+ vz)) (9)
By combining the intra- and inter-view contrastive learning, the overall loss of scope- assisted multi- view graph contrastive learning module is
C =olog Enst ep(o).u)/T
(6)
Eyri Lintre + Linter.
(10)
where is temperature parameter. We define the critic -(.,-) (im(a()))a()) where sim(-.-) is
2.6 Adaptive Multi-Graph Fusion
"Note that, u: and n; are the i-th vector of dep and H seme respoctively, defined in Scction 2.4.
Based on the graph representation of DepG and SemG, ddep and H derived by dependency

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and semantic correlation information, here we propose an adaptive multi-graph fusion module to adaptively fuse syntax dependency and semantic information for capturing syntax- and semantic- enhanced aspect-dependent output representation,
#Positve
#Negative
#Neutral
Dataset
Train
Test
Train
Test
Train
Test
LAP14
976
337
851
128
455
167
REST14
2164
727
807
196
637
196
RESTI5
912
326
36
34
256
182
de = Rel.U( dee + +HHH)m)))
(11)
RESTI6
1657
611
101
44
748
204
- p- a (g 8-1-a
(12)
TWITTER 1507
172
1528
169
3016
336
where a and are the dynamic fusing proportions, g()) is a self-gating function with a shared convolu- tional kernel. p E [0, 1] is the hyper-parameter of prior knowledge.
MAMS
3380
400
2764
329
5042
607
Table 1: Satistics six datasets of ABSA.
Since the two graph symmetric, the fused SemG representation Hem is defined similarly Hdep. Finally, we concatenate DepG repre- sentation Hdep and SemG representation Heem to obtain syntax- and semantic-enhanced aspect- dependent representation r:
where Te. 7s. and 7g are the coefficients. θ rep- resents all trainable parameters. À represents the coefficient of Le-regularization.
Experimental Setup
3.1
Datasets
deep C H em
(13)
We conduct experiments on six public standard datasets and the detailed statistics are shown Ta- ble 1. The R4sT14, LAP14, RESTI and RESTI6 datasets are from SemEval ABSA challenges (Pon- tiki et al., 2014, 2015, 2016), consisting of reviews in the restaurant and laptop domains. The TWIT- TER dataset is collection of tweets (Dong et al., 2014). The MAMS dataset is consisted of sen- tences with multiple aspects (Jiang et al., 2019). Furthermore, order to use scope labels, we use the expanded versions of these six datasets from Xu et al. (2022).
where r 2d
2.7 Model Training
Aspect-oriented Sentiment Classification. By applying average pooling on aspect node repre- sentations of r, get the final arpect-oriented representation vector z:
- -AvgPooling(ray Tag." ... Pom
Tom)
(14)
where re, denotes the i-th word in aspect of sen- tence After that, we adopt culle-coneected layer with softmax normalization to yield a proba- bility distribution of sentiment representation:
3.2 Implementation Details
(15) where ÿ E d is the predicted sentiment polarity, dp is the dimensionality of sentiment labels. W, E xdd and b, C dR are parameters to be learned. Then the cross-entropy loss between the predicted labels and the ground-truth label y of i-th instance is defined as:
softmax( W, b.)
We use Stanford parser to get syntactic depen- dency trees and employ the pre-trained BERT' in PyTorch. To alleviate overfitting, we set dropout at a rate 0.3, learning rate at x 10 of adam optimizer and L2 regulation at 10-5 for training. The layers of DepGCN and SemGCN are 2. The parameters Vo and 7s are set to 1 and 0.03 for all datasets. The sayper-parameee tively set (0.4, 0.11), ( (..22, 0.13), (0.81, 0.1). (0.2, 0.14) and (0.19, 0.12) for the five datasets. Accuracy(Acc) and macro-f1 (F1.) are evaluation metrics.
NB
dp
Yy and respec-
Lets
y log T!!
(16)
Joint Learning Objective. The learning objec- tive of our proposed model is to train model by jointly minimizing the three losses generated by sentiment classification. aspect-specific scope de- tection, and multi-view graph contrastive learning, which can be formulated
3.3 Baselines
We compare the proposed A2SMvCL with ries of strong ABSA baselines, including semantic
hnttp:://saaffodnnll siitinl...ttttiti hhttps://githii wtt..ggiiioooooee........ io/CoreNL//
= + Socope Tessopet7/00 tgf + 21|0112 (17)

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LAP14
RESTI4
RESTIS
RESTI6
TWITTER
MAMS
Model
Acc.(%)
F1((%)
Acc.((R)
FL((5)
Acc.(%)
F1.(9))
(cc.(%)
FL(())
Acc.(%)
FL.(3)
Acc(
FL(5)
BERI-SRC
80.56
77.20
84.55
75.74
83.03
63.92
90.75
74.00
73.41
72.38
82.82
81.90
SDGCN
81:35
78.34
83,57
76.47
BATAF-GRU
78.59
74.78
84,11
76.09
74.34
72.76
IMA
77.44
73.48
79.29
64.63
R-GAT
78.21
74.07
86.60
80.16
76.15
74.88
84.52
83.74
KumaGCN
81.98
78.81
86.43
80.30
86.35
70.76
92.53
79:24
77.89
77.03
ACLT
79.68
75.83
85.71
78.44
84.44
72.08
92.15
78.64
75.48
74,51
HGCN
79.39
86.45
83.91
91.72
doIGCN
81.03
78.10
86, 16
80:49
85.24
72.74
93.18
82.32
78.11
77.00
84,95
84.44
BiSyn GAT
79.43
75.07
86.70
79.57
83.39
71.72
88.82
71.75
76.51
75.34
DGEDT
79.80
75.60
86.30
80.00
84.00
71.00
91.90
79:00
77.90
75.40
BERT4GCN
77.49
73.01
84.75
77.11
83.23
67.27
87.78
75.34
74.73
73,76
DialGCN
81.80
78.10
87.13
81.16
84.69
72.97
89.87
77.26
77.40
76.02
CPA-SA
75.18
71.5
82,64
73.38
MGFN
81.83
78.26
87.31
82.37
84.40
72.66
92.04
81.57
78.29
77.27
A2SMyCI. (Ours)
82.12
78.82
87.86
82.41
86.74
75.05
93.42
83,80
78.49
77.18
85,10
84.65
Table 2: Experimental results comparison on five publicly available ABSA datasets. The results with 6_" denote that no results were reported code was not released in the original paper.
information based methods: BERT-SPC (Devlin et al., 2019), SDGCN (Zhao et al., 2020), BATAE- GRU (Wang and Wang, 2021), and IMA (Wang et al., 2022), syntax information based methods: R-GAT (Wang et al., 2020), KumaGCN (Chen et al., 2020), ACLT (Zhou et al., 2021), HGCN (Xu et al., 2022), dotGCN (Chen et al., 2022), and BiSyn-GAT (Liang al., 2022), both semantic and syntax information based methods: DGEDT (Tang et al., 2020), BERT4GCN (Xiao et al., 2021), Du- aIGCN (Li et al., 2021), CPA-SA (Huang et al., 2022), and MGFN (Tang et al., 2022).
TER dataset. These indicate the effectiveness of our proposed A2SMvCL in the ABSA task. More concretely, compared with semantic information based methods such as BERT-SPC and SDGCN, our A2SMvCL framework removes the noise in- terference from the irrelevant opinion words that could be introduced through the attention mecha- nism. Moreover, compared with the syntax infor- mation based methods, such as ACLT and dotGCN, our A2SMvCL improves significantly across all datasets on both metrics. This verifies that explor- ing scope acspe-assisted graph contrastive learning to sep- arate different aspect-related opinion words can bet- ter facilitate the capturing of aspect-dependent sen- timent clues, so improve the performance of ABSA. In addition, compared with both semantic and syntax information based methods, our model achieves better performance. This demonstrates that our model can better capture aspect-oriented semantic and syntax information as well as elim- inate the interference introduced by the attention mechanism and dependency tree structure.
In addition, we also design several variants of our proposed A2SMvCL in the ablation study. "w/o Lge denotes without all contrastive learning. "w/o Lintra" and "w/o Linter" " denote without intra- and inter-view contrastive learning, respectively. "w/o Adaptive Fusion" denotes without Adaptive Multi- Graph Fusion module. "w/o Scope" denotes with- out considering scope, simply performing graph contrastive learning on all the node representations learned by SemG and DepG.
4
Experimental Results
4.2 Ablation Study
4.1
Main Results
To further investigate the role of different compo- nents in our A2SMvCL model, we conduct exten- sive ablation studies and report the results in Ta- ble 3. We observe that removing the scope-assisted multi-view graph contrastive learning ('w/o Lget", "w/o Cintra", and "w/o Linter") sharply reduces the performance across all datasets. This demonstrates that intra- and inter-view contrastive learning based on aspect-specific scopes can help the model ignore
The main comparison results of ABSA on five datasets are reported in Table 2. It can be ob- served from the experimental results that our pro- posed A2SMvCL framework consistently outper- forms all comparison baselines on the LAP14, RESTI4, RESTIS, RESTIG 6 datasets, and MAMS datasets, and achieves an overall better perfor- mance than comparison baselines on the TwIT-

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LAP14
RESTI4
RESTIS
REST
TWITTER
Model
Acc.(%)
FL.(%)
Acc.(%)
FL.(%)
Acc.(%)
F1.(%)
Acc.(%)
F1.(%)
Acc.(%)
F1.(%)
A2SMvCL (ours)
82.12
78.82
87.86
82.41
86.74
75.05
93.42
83.80
78.49
wlo Eage
79.65
76.48
86.05
80.38
84.86
71.83
92.11
79.37
77.18
75.58
w/o Cintra
79.97
76.72
86.96
80.84
85.79
72.03
93.09
83.10
77.88
76.65
wlo Cinter
80.29
77.32
86.16
80.49
72.64
92.43
82.78
77.18
76.18
wlo Scope
80.57
77.40
86.70
81.42
85.80
74.77
92.27
80.57
78.20
76.86
w/o Adaptive Fusion
81,41
77.60
87.14
81.57
86.36
74.64
92.76
82.45
77.47
76.00
Table 3: Experimental results of ablation study.
0.8
Siln Siapp
Latign
0.8
0.5
(a) ACC
(h) F1
0.
Figure 3: Results of model that training on the TWIT- TER dataset, and testing the trained model on the other four datasets.
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Luni form
the noise interference from other aspect-specific scopes, improving the quality of assecctdependent sentiment representation which in turn results in a better performance of the ABSA task. It can also be observed that the performance degrades significantly all datasets when removing the scope (w/o Scope) and performing vanilla graph contrastive learning on all node representations of two graph views. This indicates that scope can ef- fectively separate aspect-based context words. Ad- ditionally, A2SMvCL without adaptive multi graph fusion module ("w/o Adaptive Fusion") performs slightly worse. which implies that incorporating DepG and SemG representations together can fur- ther improve the performance of ABSA.
Figure 4: Visualization contrastive representation. The darker the color of the point, the greater the curacy. The arrows indicate the training direction. Bottom-left better.
4.4 Analysis
Analysis of Contrastive Representation. To further analyze how the proposed A2SMvCL works in contrastive representation learning, train the four variants of A2SMvCL and A2SMvCL it- self and visualize the alignment and uniformity metrics in Figure 4. We follow Wang and Isola (2020) and use the same evaluation metrics, align- ment Lulign and uniformity Luniform. where lower Calign and Luniform denotes better performance of the model. Compared with the four variants of A2SMvCL, our model has the lowest Latign and Luniform during training, which indicates that our A2SMvCL attains strong ability in contrastive learning. Compared with "w/o Lintra" " and "w/o Linter "), A2SMvCL w/o Scope presents worse alignment and uniformity. indicating that scope is helpful to obtain better aspect-dependent sen- timent representation. This implies the effective- and superiority of our proposed scope- multi-view graph contrastive learning.
4.3 Robustness Study
We further conduct experiments in the cross- domain scenario to analyze the robustness of our A2SMvCL. To be more specific, we first train a source model on the TWITTER dataset and test its performance on the other four datasets. As shown in Figure 3, our model outperforms three compari- methods across all four datasets, illustrating the strength of our proposed A2SMvCL in learning the aspect-dependent sentiment representation. This implies that the scope-assisted multi-view graph contrastive learning framework has strong robust- ness for grasping aspect-opinion interactions.
Case Study. To investigate how A2SMvCL works eliminating noisy interference from other

---

servic
people
comm
0003
D0e4
00641
Guaza
0:422
Service
slow
but
people
were
friendly
pasta
00043
21384
9044
00834
ccnri
Food
Food
average while
pasta
world
Figure 5: Case study of samples with multiple aspects. (a) Both "service" and "people" are aspects. (b) "pasta" and "food" are two aspect terms.
(a) BiSyn-GAT
(b) A2SMvCL (Ours)
Figure 6: Visualization of intermediate embeddings the LAP14 14 dataset. Blue dots denote Positive examples, dots denote Negative examples, and green dots de- note Neutral examples. More results in Appendix B.
aspect-related opinions, conduct case study and visualize the attention weights of words for each aspect in Figure We can observe that the weights between aspect and its corresponding opin- ion words are higher than ones between aspect and uuppeattn words (like "service" and "slow", "service" and "friendly"). This implies that our pro- posed A2SMvCL can indeed separate the opinion words from distinct aspect-related words and ac- curately grasp the aspect-opinion interactions, so as to improve the performance of ABSA. Due to the page limitation, we place more case studies in Appendix A.
level semantic interaction between aspect and con- text. Some methods use syntax dependeney trees to model the syntactic relationship between aspect and its corresponding opinion. For example, Zhang et al. (2019), Wang et al. (2020) and Liang et al. (2022) utilize aspect-oriented dependency trees to capture the syntactic relationship with graph neural network. Chen al. (2020) uses GCN to incorpo- rate syntactic information and HardKuma-base la- tent graph information. Xu et al. (2022) introduces the concept of scope, which outlines a structural text region related to specific aspect.
Visualization. To more intuitively verify the ef- fectiveness of our model, we utilize t-SNE (Van der Maaten and Hinton, 2008) visualize the interme- diate embeddings of aspect-dependent sentiment representations learned by BiSyn-GAT and our pro- posed A2SMvCL on LAP14 dataset. As shown in Figure 6, it can be seen that the embeddings within the same sentiment are more aggregated and the boundaries between different sentiments are more distinct for A2SMvCL. This verifies that A2SMvCL can derive more definite correlations and clearer differences among different sentiments, which facilitates the ABSA task. See more visual- ization samples in Appendix B.
Moreover, some works (Tang et al., 2020; Li et al., 2021; Tang al., 2022) utilize GCN to ef- fectively fuse syntactic and semantic information to obtain richer aspect-oriented sentiment repre- sentation. Our work shares a similar syntactic and semantic information extraction approach, however we focus on designing the multi-view graph con- trastive learning to capture the significant aspect- dependent sentiment clues to improve ABSA task.
6 Conclusion
We have re-examined the challenges encountered by existing models for ABSA, and pointed out the suitability of the scope-assisted graph contrastive learning for addressing these issues. Accordingly, we have proposed novel uspect-to-scop oriented multi-view contrastive learning framework to mit- igate noisy interference, and leverage the correla- tions and differences among different sentiment polarities and different types of information (syn- tactic dependency and semantic correlation) Ex- tensive experiments on the six benchmark datasets have demonstrated the effectiveness and superiority of our proposed model.
5 Related Works
Early efforts ABSA mainly use the traditional machine learning techniques, such as ofg-of-words, SVM, and so on, to manually extract engineered features to classify the sentiment (Wang et al., 2011; Hegde and Seema, 2017). Recently, some methods use attention mechanism to explore the word-level semantic correlations between contex- tual words (Chen et al., 2017: Fan et al., 2018; Gu et al., 2018; Devlin et al., 2019; Zhao et al., 2020; Wang and Wang, 2021; Wang et al., 2022; Xiao et al., 2).1), which aims at capturing the word-

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Limitations
Feifan Fan, Yansong Feng, and Dongyan Zhao. 2018. Multi- -laiiegrained attention network for aspect-level sen- timent classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Lan- guage Processing, EMNLP, pages 3433-3442.
The major limitation is that our method is not suit- able for simultancously identifying multiple senti- ment polarities of multiple aspects in one sentence. Actually, this is a common limitation of existing ABSA methods. In our A2SMvCL framework, we generate semantic and syntax graphs. Both two graphs can provide richer information to advance a better performance of ABSA task. For an input sentence with very long text, this could result in increasing of the number of generated graph nodes. Accordingly, the training time of our model will increase, which would prevent our A2SMvCL from applying to the scenarios with document data.
Shuqin Gu, Lipeng Zhang, Yuexian Hou, and Yin Song. 2018. A position-aware bidirectional atten- tion network for aspect-level sentiment analysis. In Proceedings of the 27th International Conference on Computational Linguistics, COLING, pages 774- 784.
Rajalaxmi Hegde and Scema. 2017. Aspect based feature extraction and sentiment classification of review data sets using incremental machine lear- ing algorithm. In 2017 Third International Confer- ence on Advances in Electrical, Electronics, Infor- mation, Communication and Bio-Informatics (AEE- ICB), pages 122-125. IEEE
Acknowledgements
This work was supported by the National Natu- ral Science Foundation of China (No. 62076079), and The Major Key Project of PCI(Graatt No. PCL2022A03), and Guangdong Provincial Key Laboratory of Novel Security Intelligence Tech- nologies 2002212120100055 and Shenzhen Science and Technology Program (Grant No. ZDSYS2021002309 1809029).
Bo Huang, Ruyan Guo. Yimin Zhu, Zhijun Fang, Guo- hui Zeng, Jin Liu, Yini Wang, Hamido Fujita, and Zhicai Shi. 2022. Aspect-level sentiment analy- sis with appect-specific context position information. Knowl. Based Syst., 243.
Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, and Min Yang. 2019. A challenge dataset and ef- fective models for aspect-based sentiment analysis. In Proceedings of the 2079 Conference Empiri- cal Methods Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-JJCNLP 2019, Hong Kong, China, November 3-7, 2019, pages 6279- 6284. Association for Computational Linguistics.
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# Sentence
DualGCN
BiSyn-GAT (Nv)
A2SMvCL
Other than the crappy service from two individuals, great.
(PX)
(Nv)
2 Lven fancy ingredients don't make lor good pizza unless someone knows how to get the crust right.
(PX)
(PX)
(NV)
Service is highly refined but our seating was delayed
3
(PV,
(PV,NV)
(PV.NV)
35 minutes past our reservation.
4 Usually the waiters are kind enough to split the dish in half.
PX)
(PV.PX)
(PV.OV)
food is all-around good, with the rolls usually
5 excellent and the sushi not quite on the same level.
(PV.PV.P
(PV.PV.P
(PV.PV.NV)
Not only the service great, but atmosphere can easy to form conversation around table.
(PV.NX)
(P..NX)
(PV.P()
Table 4; Case studies our A2SMvCL model compared with other baselines.
(a) BiSyn-GAT
(b) A2SMvCI (Ours)
(a) BiSyn-GAT
(b) A2SMvCI (Ours)
Figure 7: Visualization of intermediate embeddings on
Figure 8: Visualization of intermediate embeddings on TWITTER dataset.
the RESTI4 14 dataset.
A More Case Studies
alGCN and BiSyn-GAT models align aspect "sushi" and sentiment expression "excellent", and thus pre- dict its sentiment polarity is positive, which is the wrong result. Because the expression "excellent") is the corresponding opinion word of aspect "rolls". Both DualGCN and BiSyn-GAT models are mis- lead by the sentiment expressions related to other aspects. Therefore, compared with other models, our A2SMvCL model can directly climinate the interference of aspect-unrelated words and capture significant aspect-dependent sentiment clues more accurately. In addition, these cases also demon- strate that our A2SMvCL model can achieve better performance in the scenario with multiple aspects.
Table shows the aspect-based sentiment predic- tions of some cases by using different compari- son models, where the symbols P, N, and O rep- resent positive, negative, and neutral sentiments respectively. The words with red color and blue color represent the aspect and its corresponding opinion expressions respectively. Note that, the samples with id from #3 to #6 contain multiple aspects, in which the fifth sentence contains three aspects. For the single aspect case (like second sentence #2), only our proposed A2SMvCL model predicts it as negative. For DualGCN that using attention mechanism and BiSyn-GAT that using dependency tree, they fail to capture the impor- tant sentiment clues "don't", representing negative sentiment. For a sentence that contains two as- pects (like fourth sentence #4), when the aspect is "dish" and does not have explicit opinion ex- pressions, the DualGCN and BiSyn-GAT models may suffer from the noisy interference introduced by attention mechanism and syntactic dependency tree, leading to the deviation in terms of locating aspect and its corresponding expressions. For sentence that contains three aspects (like fifth sen- tence #5), when the aspect "sushi" and it does not also have explicit opinion expressions, both Du-
B More Visualization Results
Figure 7 and Figure show the visualization results on RESTI4 14 and TWITTER datasets. We show the t-SNE visualization of intermediate embeddings learned by BiSyn-GAT and our A2SMvCL mod- els. Again, the visualization results show that the embeddings within the same sentiment are more aggregated, and the boundaries between different sentiments are more distinct. This verifies that A2SMvCL can derive more definite correlation and clearer difference among different sentiments, which is more conducive to the ABSA task.


Reviews:
[{"review_id": "FtLfh2Xqcw", "reviewer": "Reviewer_WYcF", "summary": "", "questions": "A. Figure 1 (a) shows the attention scores for the anchor aspect “people” to its context. How does these attention scores get?\n\nB. As mentioned in Reasons To Reject, could you give some explanation about the extra annotation problem?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your encouraging words and constructive comments. We sincerely appreciate your time in evaluating our work. Our point-to-point responses to your comments are given below.\n\nQ1: The scope labels need extra annotation. This means that the method proposed in this paper requires more expensive labour costs for practical application....\n\nA1: Thanks for the comments. The scope labels can be acquired automatically by using the annotation tool [1]. In this paper, the used scope-assisted datasets are provided by Lvxiaowei Xu et al. [1]. \n\n[1] Lvxiaowei Xu, Xiaoxuan Pang, Jianwang Wu, Ming Cai, and Jiawei Peng. 2022. Learn from structural scope: Improving aspect-level sentiment analysis with hybrid graph convolutional networks. arXiv preprint arXiv:2204.12784.\n\nQ2: Of course, if scope labels can be annotated fully automatically, the above problem does not exist. However, in the paper presenting the dataset with scope labels[1], it says \"According to our statistics, 26.4\\% of the samples require manual adjustments.\" I am concerned that the effectiveness of the method in this paper may be compromised by low-quality automatically annotated scope label.\n\nA2: We appreciate your concern regarding the annotation of scope labels and the potential impact of low-quality automatic labels on the effectiveness of our method. We partially agree with you and the low-quality annotated data may affect the effectiveness of the AI model. Thanks to their efforts [1], [1] published the high-quality and manually adjusted annotated scope datasets, such as \\textsc{Lap14}, \\textsc{Rest14}, \\textsc{Rest15}, \\textsc{Rest16}, and \\textsc{Mams} datasets. We use these high-quality datasets to develop a robust model that can handle some annotation errors, such as using \\textit{graph structure} to handle grammatical connection interruption errors and using the \\textit{Adaptive Multi-graph Fusion} strategy to handle the representation error caused by misannotation scope label. For short-sighted errors, it is hard to handle since scope fails to capture implicit transitive descriptions related to the target term. This may affect the model's performance.\n\nTo further illustrate the robustness of our proposed method, we conduct a cross-domain experiment. we first train a source model on the \\textsc{Mams} dataset and test its performance on the other four datasets shown in Figure 3. The results illustrate that our proposed approach outperforms three comparison methods across all four datasets, illustrating the robustness of our model A2SMvCL in learning the aspect-dependent sentiment representation. This implies that the scope-assisted multi-view graph contrastive learning framework has strong robustness for grasping aspect-opinion interactions.\n\nQ3: Figure 1 (a) shows the attention scores for the anchor aspect “people” to its context. How does these attention scores get?\n\nA3: Thanks for the comments. We construct a simple model, a pre-trained BERT-base followed by a self-attention layer, to calculate the attention scores of aspect terms.\n\nQ4: As mentioned in Reasons To Reject, could you give some explanation about the extra annotation problem?\n\nA4: We give the detailed explanation in **A2**.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper is well structured and clear to follow. \n\nThe scope-assisted multi-view (intra- and inter-view) graph contrastive learning effectively extracts high-quality features and this approach can be extended to other tasks.\n\nThe proposed model achieves the state-of-the-art on several datasets and verifies the effectiveness.", "reasons_to_reject": "The scope labels need extra annotation. This means that the method proposed in this paper requires more expensive labour costs for practical application, and makes the comparison between this method and baseline methods less fair. \n\nOf course, if scope labels can be annotated fully automatically, the above problem does not exist. However, in the paper presenting the dataset with scope labels[1], it says \"According to our statistics, 26.4% of the samples require manual adjustments.\" I am concerned that the effectiveness of the method in this paper may be compromised by low-quality automatically annotated scope label.\n\n[1] Lvxiaowei Xu, Xiaoxuan Pang, Jianwang Wu, Ming Cai, and Jiawei Peng. 2022. Learn from structural scope: Improving aspect-level sentiment analysis with hybrid graph convolutional networks. arXiv preprint arXiv:2204.12784.", "questions_to_authors": "A. Figure 1 (a) shows the attention scores for the anchor aspect “people” to its context. How does these attention scores get?\n\nB. As mentioned in Reasons To Reject, could you give some explanation about the extra annotation problem?", "paper_topic_and_main_contributions": "This paper propose a scope-assisted multi-view graph contrastive learning framework called A2SMvCL for ABSA task. Experiments on six benchmark datasets show that this approach outperforms state-of-the art methods.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "G7L5llhef3", "reviewer": "Reviewer_etwT", "summary": "", "questions": "I think the improvement over KumaGCN is tiny on several datasets. Can you give some further analysis?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your time in reading the paper, and our point-to-point responses to your comments are given below.\n\nQ1: I would like to challenge this because the dependency tree is not used in many ABSA methods, so the problem proposed in this paper may not be an important issue for a lot of ABSA methods.\n\nA1: Sorry for the confusion. A writing error leads to a misunderstanding. Existing ABSA methods use either the dependency tree technique or the attention mechanism technique to improve the performance of ABSA. Either of these two techniques may bring up noisy interference problems. \nIndeed, the dependency tree is not used in all ABSA methods. But, the attention mechanism technique is very common in ABSA. Most ABSA methods use attention mechanism to capture the aspect-related semantic \n information. Therefore, the proposed problem in this paper is an important issue in ABSA methods.\n\nQ2: I think the improvement over KumaGCN is tiny on several datasets. Can you give some further analysis?\n\nA2: Thanks for the comments. KumaGCN used the dependency tree to obtain syntactic dependencies representation and the attention mechanism to derive the latent graph. Our proposed A2SMvCL approach obtains a tiny improvement over KumaGCN on \\textsc{Lap14} and \\textsc{Twitter} datasets. The major reason is that these two datasets contain fewer multi-aspect sentences: \\textsc{Lap14} dataset has 36\\% multi-aspect sentences, and \\textsc{Twitter} dataset does not contain multi-aspect sentences. Therefore, the latent graph proposed by KumaGCN can accurately locate the aspect term and its corresponding opinion expression, resulting in comparable performance on single-aspect datasets.\n    Moreover, our A2SMvCL significantly improved compared to the KumaGCN method on other datasets that contain more multi-aspect sentences. This demonstrates that when multiple aspects exist in a sentence, KumaGCN may suffer from noisy interference caused by multiple aspects. \n    We hope that this explanation clarifies the confusion behind the KumaGCN method. We will include these details in the revised manuscript for better understanding.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper provides a comprehensive approach for aspect-based sentiment analysis, considering both syntax and semantics.\n2. The paper introduces a scope-assisted multi-view graph contrastive learning approach, which aligns aspect and opinion words while separating in-scope and out-scope words.\n3. The paper presents intra-view and inter-view contrastive learning techniques to enhance the performance of sentiment analysis.\n4. The experimental results demonstrate that the proposed framework outperforms several comparison baselines on multiple datasets, indicating its effectiveness in the ABSA task.", "reasons_to_reject": "It says in the abstract \"Most existing ABSA methods... suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence\". I would like to challenge this because the dependency tree is not used in many ABSA methods, so the problem proposed in this paper may not be an important issue for a lot of ABSA methods.\n", "questions_to_authors": "I think the improvement over KumaGCN is tiny on several datasets. Can you give some further analysis?", "paper_topic_and_main_contributions": "The paper discusses the construction of syntax-based and semantic-based graphs for aspect-dependent sentiment analysis. The paper proposes the use of GCN encoders to capture syntactic and semantic representations. It also introduces a scope-assisted multi-view graph contrastive learning approach to align aspect and opinion words while separating in-scope and out-scope words. The paper further describes intra-view and inter-view contrastive learning techniques. Extensive experiments show effectiveness and robustness.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "PzMJPpwDnx", "reviewer": "Reviewer_PEgC", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your encouraging words and constructive comments. We sincerely appreciate your time in evaluating our work. Our point-to-point responses to your comments are given below.\n\nQ1: It is hard to connect the problem to model building. There could be multiple paths to address the problem, but it is not clear why selecting graphs and contrastive learning. \n\nA1: Thanks for the comments. The main challenge of ABSA is how to accurately locate the aspect term and its corresponding opinion expressions while preventing interference from irrelevant context words. The graph structure is a very intuitive and effective way of modeling the relationship between word and word. Therefore, many ABSA methods use the graph to boost their performance, demonstrating the superiority and effectiveness of graphs in capturing the semantic representation of sentences. Consequently, we use the graph to help capture richer semantic information from sentences.\n\nThe motivation of this paper is that when multiple aspect terms exist in a sentence, the opinion expressions corresponding to these aspect terms may interfere with each other, resulting in false predictions. Accordingly, we use the aspect-specific scope to separate different aspect-related context words to prevent noisy interference from other aspects.  However, relying solely on the aspect-specific scope is inadequate for capturing a richer aspect-dependent sentiment representation, given that each aspect-specific scope carries limited semantic and syntactic information. \nTherefore, we propose a multi-view graph contrastive learning approach to enhance aspect-dependent sentiment-aware representation by integrating syntactic dependency and semantic correlation information.\n\nQ2: For example, if the noise is introduced by syntax tree and/or attention maps, why build contrastive learning on top of H rather than the input of H, i.e. eliminating noise from the early stage.\n\nA2: Thanks for the comments. We need to leverage syntax tree and attention mechanism techniques to capture syntactic and semantic dependency relationships in a sentence, respectively. After that, we use contrastive learning to enhance the aspect-dependent sentiment representation, so as to obtain high-quality aspect-dependent sentiment representation. If we build contrastive learning on the input of ${H}$, it may limit the ability of the model to acquire high-quality aspect-dependent syntactic and semantic dependency features, thus hindering the learning of a more nuanced aspect-dependent, sentiment-aware representation.\nWe hope that this explanation clarifies the rationale behind our approach. We will include these details in the revised manuscript for better understanding.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Improved performance on six benchmark datasets over various baselines. The improvements seem to be significant. \n2. Thorough experiments and analysis.", "reasons_to_reject": "1. It is hard to connect the problem to model building. There could be multiple paths to address the problem, but it is not clear why selecting graphs and contrastive learning. For example, if the noise is introduced by syntax tree and/or attention maps, why build contrastive learning on top of H rather than the input of H, i.e. eliminating noise from the early stage.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper studies aspect-based sentiment analysis (ABSA) and focused on eliminating noise introduced by the attention mechanism and dependency tree. To address the issue, the paper proposed a scope-assisted multi-view group contrastive learning framework (A2SMvCL), which contains five components: 1. aspect scope detection, 2. graph generation, 3. contrastive learning, 4, graph fusion, and 5. aspect-oriented sentiment classification.  On six benchmark datasets, the paper reports improvements against various baselines.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "7zzxQ2TobG", "reviewer": "Reviewer_viZs", "summary": "", "questions": "1. In the experience, did the authors use BERT-base or BERT-large?\n2. How did the authors tune the hyper-parameters?\n3. Did the author try other parsers to get the dependencies? Will the quality of the dependencies affect model performance?\n", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your time in reading the paper, and our point-to-point responses to your comments are given below.\n\nQ1: The techniques used in the paper are not new. The approach is complicated and basically is the combination of existing techniques with minor changes.\n\nA1: Thanks for the comments. and we partially agree with you. The use of syntactic and semantic feature, as well as graph-based approaches, is widely used in ABSA research. Our main contribution is not to improve these existing techniques. We employ these techniques to help obtain the syntactic and semantic dependency relationship. We focus on addressing the challenge that when multiple aspect terms exist in a sentence, the opinion expressions corresponding to these aspect terms may interfere with each other, resulting in false predictions. \n\nTo address this issue, we use the aspect-specific scope to separate different aspect-related context words to prevent noisy interference from irrelevant context words. Meanwhile, we propose a novel multi-view graph contrastive learning framework to accurately align each aspect and its corresponding opinion expressions so that obtains semantic- and syntactic-enhanced aspect-oriented sentiment representation. \n\nThis is the first work to leverage contrastive learning to fuse different types of information within a sentence while separating different aspect-dependent sentiment representations in syntactic and semantic representation space. Moreover, the construction of positive/negative samples and contrastive learning strategy are novel. We first define positive/negative samples from a new viewpoint on whether words are in/out aspect-specific scopes. Then, we propose intra- and inter-view contrastive learning objects to facilitate the learning of the richer aspect-dependent and sentiment-aware representation, which is also novel in the social media text analysis area.\n   \nWe hope that this explanation clarifies the novelty of our approach.\n\nQ2: It is not clear how the hyper-parameters are tuned. It seems the result reported in the paper comes from a single run (the authors do not report the average and standard deviation of the results). Given the test sets of the benchmark datasets are small, it is possible that the model overfits the test set.\n\nA2: We appreciate the question and would like to explain the confusion about the experimental details. The results in Table 2 are the average performance over five runs by using different seeds. We have revised the results in Table 2 and supplemented the standard deviation of the results. The performance of our A2SMvCL on the **Accuracy** metric is presented below.\n $$\n\\begin{array}{ccccccc} \n\\text{Model} & \\text{Lap14} & \\text{Rest14} & \\text{Rest15} & \\text{Rest16} & \\text{Twitter}  & \\text{MAMS} \\\\\\\\\n\\hline\n\\text{A2SMvCL(Ours)} & 82.12\\pm0.27 & 87.86\\pm0.22 & 86.74\\pm0.56 & 93.42\\pm0.28 & 78.49\\pm0.74 & 85.10\\pm0.30 \\\\\\\\\n \\end{array}\n$$\nThe performance of our A2SMvCL on the  **F1-score** metric is presented below.\n $$\n\\begin{array}{ccccccc} \n\\text{Model} & \\text{Lap14} & \\text{Rest14} & \\text{Rest15} & \\text{Rest16} & \\text{Twitter}  & \\text{MAMS} \\\\\\\\\n\\hline\n\\text{A2SMvCL(Ours)} & 78.82\\pm0.18 & 82.41\\pm0.27 & 75.05\\pm2.13 & 83.80\\pm1.25 & 77.18\\pm0.13 & 84.65\\pm0.21 \\\\\\\\\n \\end{array}\n$$\n\nSimilar to all comparison methods, we follow the standard training/testing set split to conduct all experiments. Although the testing sets are small, the dataset publisher can ensure that they were randomly chosen and should represent the distribution of the entire dataset. Moreover, we tested our proposed A2SMvCL method on different datasets to comprehensively evaluate its performance. \n\nIn addition, during the experimental phase, the model's training and testing processes were independent. We did not use the test set for any kind of parameter tuning, thereby ensuring the model's generalization to the test set.\n\nQ3: Many related studies are not discussed. (examples are in missing references).\n\nA3: We appreciate the suggestion and will cite and analyze these works in our revision.\n\nQ4: In the experience, did the authors use BERT-base or BERT-large?\n\nA4: Thanks for the comments. In this paper, for a fair comparison, we follow the existing ABSA methods and use the identical **BERT-base** model as the text encoder.\n\nQ5: How did the authors tune the hyper-parameters?\n\nA5: Thanks for the comments. Due to some mistakes in our writing, there is ambiguity about hyper-parameters. In fact, we only introduce two hyper-parameters $\\gamma_g$ and $\\tau$ to control and balance our proposed scope-assisted multi-view GCL strategy. The $\\gamma_c$ and $\\gamma_s$ are not hyper-parameters and are set empirically as 1 and 0.03, respectively, according to the HGCN model [1].\n    We adopt the Grid Search technique to tune the hyper-parameters. We empirically select a set of possible values for the hyper-parameters, then perform combinations on these values to find the optimal hyper-parameter combination on all datasets.\n    Grid Search technique is a common parameter tuning technique, and it can systematically explore all possible combinations to find the optimal values of hyper-parameters.\n\nQ6: Did the author try other parsers to get the dependencies? Will the quality of the dependencies affect model performance?\n\nA6: Thanks for the comments. To evaluate the impact of different parsers, we conduct an experiment based on our proposed A2SMvCL by using two common dependency parsers, Stanford Parser [2] and Biaffine Parser [3]. The results are presented below. The best results are bold. From the following table, we can find that using Stanford Parser can obtain better performance of ABSA task. Moreover, these two dependency parsers have little impact on the performance of our proposed A2SMvCL method.\n $$\n\\begin{array}{cccc} \n\\text{Parser} & \\text{Lap14} & \\text{Rest14} & \\text{Twitter} \\\\\\\\\n\\hline\n\\text{Stanford} & \\boldsymbol{82.12} & \\boldsymbol{87.86} & \\boldsymbol{78.49} \\\\\\\\\n\\text{Biaffine} & 81.97 & 87.23 & 78.08 \\\\\\\\\n \\end{array}\n$$\n\n[1] Lvxiaowei Xu, Xiaoxuan Pang, Jianwang Wu, Ming Cai, and Jiawei Peng. 2022. Learn from structural scope: Improving aspect-level sentiment analysis with hybrid graph convolutional networks. arXiv preprint arXiv:2204.12784.\n\n[2] Danqi Chen and Christopher Manning. A fast and accurate dependency parser using neural networks. EMNLP.\n\n[3] Timothy Dozat and Christopher D Manning. Deep biaffine attention for neural dependency parsing. ICLR.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper proposed an approach for ABSA\n2. The proposed approach achieved good results on six English benchmark datasets.\n", "reasons_to_reject": "1. The techniques used in the paper are not new. Both syntactic and semantic features are extensively employed in existing ABSA research. Additionally, graph-based approaches are also widely used for encoding these features. Furthermore, contrastive learning is not an innovative concept.\n2. The approach is complicated and basically is the combination of existing techniques with minor changes.\n3. Some important technique details are missing. (see questions)\n4. It is not clear how the hyper-parameters are tuned. It seems the result reported in the paper comes from a single run (the authors do not report the average and standard deviation of the results). Given the test sets of the benchmark datasets are small, it is possible that the model overfits the test set.\n5. Many related studies are not discussed. (examples are in missing references)", "questions_to_authors": "1. In the experience, did the authors use BERT-base or BERT-large?\n2. How did the authors tune the hyper-parameters?\n3. Did the author try other parsers to get the dependencies? Will the quality of the dependencies affect model performance?\n", "paper_topic_and_main_contributions": "The paper proposes an approach named aspect-to-scope multi-view contrastive learning (A2SMvCL) to improve Aspect-Based Sentiment Analysis (ABSA), where syntactic and semantic knowledge are leveraged through graph-based method. The model also contrastive learning to distinguish between aspect-related and unrelated sentiment representations. Experiments conducted on six public benchmark datasets show good performance of the proposed A2SMvCL.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "1. Binxuan Huang and Kathleen M Carley. Syntax Aware Aspect Level Sentiment Classification with Graph Attention Networks. EMNLP-2019\n2. Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen. Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation. EMNLP-2020: findings\n3. Yuanhe Tian, Guimin Chen, Yan Song. Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble. NAACL-2021 "}}]

Author rebuttal:

Meta-review: ABSA is such a well-explored problem. The authors focus on eliminating the "noisy interference" issues in ABSA introduced by usual methods (dependency tree, attention) by proposing a scope-assisted multi-view graph contrastive learning framework. The paper is comprehensive, structured and easy to follow. The reviewers agree that the authors achieved state-of-the-art results on multiple datasets. In terms of the novelty of the task, as I mentioned, it is a well-explored one; however, their methods supposedly overcome the interference problem. The responses made by the authors to the reviewer's questions seem justified. The paper might find an audience.
Decision: Accept-Findings