Dual-Channel Span for Aspect Sentiment Triplet Extraction
Authors: Pan Li
Ping Li
Kai Zhang
Venue: EMNLP 2023 Main (2023)
Keywords: aspect sentiment triplet extraction
dual-channel
span generation
noise reduction

Abstract: Aspect Sentiment Triplet Extraction (ASTE) is one of the compound tasks of fine-grained aspect-based sentiment analysis (ABSA), aiming at extracting the triplets of aspect terms, corresponding opinion terms and the associated sentiment orientation. Recent efforts in  exploiting  span-level semantic interaction shown superior performance on ASTE task. However, most of the existing span-based approaches suffer from enumerating all possible spans, since it can introduce too much noise in sentiment triplet extraction. To ease this burden, we propose a dual-channel span generation method to coherently constrain the search space of span candidates. Specifically, we leverage the syntactic relations among aspect/opinion terms and the associated part-of-speech characteristics in those terms to generate span candidates, which reduces span enumeration by nearly half. Besides, feature representations are learned from syntactic and part-of-speech correlation among terms, which renders span representation fruitful linguistic information. Extensive experiments on two versions of public datasets demonstrate both the effectiveness of our design and the superiority on ASTE/ATE/OTE tasks~\footnote{We release our code at \url{https://github.com/bert-ply/Dual_Span}}.

Dual-Channel Span for Aspect Sentiment Triplet Extraction
Pan Li Ping Li Kai Zhang²
School of Computer Science, Southwest Petroleum University, Chengdu, China School of Computer Science and Technology, East China Normal University, Shanghai, China (1p970717, dping. Aigaaiill......mmmmmmmmmmm....l com zk1 9800hotmail..... com
Abstract
the sentence "My vegetable risotto was burnt, and infused totally in burnt flavor", there are two sentiment triplets, namely, ("vegetable risotto", "burnt", Negative" ") and ("flavor", "burtt,, Nega- tive"), where "vegetable risotto" and "flavor" are aspect terms, "burnt" is the opinion term corre- sponding to the aspect of interest, and "Negative" is the sentiment polarity of these two triplets.
Aspect Sentiment Triplet Extraction (ASTE) is one of the compound tasks of fine- ane-grained aspect-based sentiment analysis (ABSA), aim- ing at extracting the triplets of aspect terms, corresponding opinion terms and associated sentiment orientation. Recent efforts in exploit- ing span-level semantic interaction have shown superior performance on ASTE task. However, span-bas haanhaaed approaches could suffer from ex- cessive noise due to the large number of spans that have to be considered. To this bur- den, we propose dual-channel span generation method to coherently constrain the search space of span candidates. Specifically, we leverage the syntactic relations among aspect/opinion terms and their part-of-speech characteristics generate useful span candidates, which em- pirically reduces span enumeration by nearly a half. Besides, the interaction between syn- tactic and part-of-speedd views brings relevant linguistic information to learned span represen- tations. Extensive experiments datasets demonstrate both the effectiveness of our design and the superiority on ASTE task !
My PRPS
Isegetable
NN
NN
VIN
VIN
BB
DT
Sentiment Tripletc
vegetable rivils
lunsi
Negaive
Baver
Negatia
Figure A sentence with dependency tree and part-of- speech in ASTE task.
When the idea of ASTE was first proposed. two-stage pipeline method (Peng et al., 2020) was developed for this task. However, staged process- ing scheme often lead to error propagation between subtasks. More than that, opinion terms are gen- crally associated with the aspect target, staged pipeline method breaks this interaction. To ad- dress those issue, some end-to-end approaches (Wu et al., 2020a; Xu et al., 2021; Chen et al., 202 lb, 2022b) are devised, which attempt to simultane- ously extract aspect-opinion pairs and perform sen- timent classification by introducing novel tagging schemes. In particular, most of existing end-to- end models (Wu et al., 2020a; Chen et al., 202 lb, 2022b) build the interaction between aspect and its corresponding opinion at token-level, i.e., word-to- word interactions. Despite of its efficacy, it is hard to guarantee the consistency of predicted sentiment polarity between multiple word-to- word-to-word pairs when many aspects/opinions are expressed using multi- ple words. On account of this, recent work (Xu et al., 2021; Chen et al., 2022d) adopt span-level interactions in the sentiment triplet structure. Com- pared with the token-level pairing, span-level in-
two public
1 Introduction
Aspect Sentiment Triplet Extraction (ASTE) is a compound task in fine-gran tne-grained Aspect-Basse Sen- timent Analysis (ABSA) (Pontiki et al., 2014). It is composed of three fundamental subtasks: As- pect Term Extraction (ATE) (Yin al., 2016; Ma al., 2019; Chen and Qian, 2020; Li al., 2020), Opinion Term Extraction (OTE) (Yang and Cardie, 2012, 2013; Wan et al., 2020) and Aspect Senti- ment Classification (ASC) (Wang et al., 2016; Tang et al., 2016; Xue and Li, 2018; Tang et al., 2020; Li et al., 2021). In particular, ASTE aims to ex- tract the sentiment triplet of aspect terms, corre- sponding opinion terms and their associated senti- ment polarity in given sentence. For example, in
Corresponding author: 'Wo release our code at https: //gi www.giitub..oooooo com/ dert-ply/Dual_Span
com

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teraction is proved to bring significant gains the model.
graph attention networks (RGAT) separately learn high-order syntactic dependency between words/spans and linguistic features constructed part-of-speech relations among words. Then a gat- ing mechanism is adopted to fuse the syntactic and lexical information of span candidates, which helps to enhance the feature representation of spans. On the other hand, instead of enumerating all possible spans, the span candidates extracted from two channels, i.e., the syntactic dependency relations and part-of-speech based relations, thus largely re- ducing the noisy information favor valid span pairing. Our main contributions are as follows:
However, one prominent problem with span- based methods is that they usually enumerate all spans in a sentence, which will bring about high computational cost and many noises. Specifically, the number of enumerated spans for sentence of length is O(n²), while the number of possible interactions between all opinion and aspect candi- date spans is O the later span-pairing stage. implying lot of invalid aspect/opinion spans and span pairs. Moreover, most of the existing span- based methods model direct interactions between two spans. The high-order interactions are gener- ally overlooked.
We devise a dual-channel span generation method for aspect sentiment triplet extraction, which produces a span candidate set much smaller than the greedily enumerated one by leveraging the syntactic dependency and part- of-speech correlation among tokens/spans in a dual-channel manner.
To address those issues, we explore the linguis- tic phenomena in the spans. Our observations are two-fold: First, multiple words composed of the span of aspect/opinion target are generally syn- tactically dependent, and multiple dependency re- lations can transmit higher-order interactions be- tween spans. For example, in Figure 1, the aspect term "vegetable risotto" has an intra-span syntactic dependency 'ccoppunnd" and an inter-span depen- dency "nsubj"" with "burnt". On the other hand, the span "flavor" is indirectly related to the first "burnt" (i.e., the one associated with "vegetable risotto") within 2 hops in the syntactic tree. This indirect relation may suggest the relevance of the sentiment polarity of the two span pairs, namely, ("vegetable risotto", "burnt") and ("flavor", "burnt"). fect, the ground truth of sentiment polarity of these two aspect-opinion pairs are the same, as shown in Figure 1.
We construct the intra-span and inter-span lations based the part-of-speech correlation of spans/words, on top of which the high-order linguistic interactions is able to be captured by relational graph neural networks.
We combine the syntactic information learned from dependency tree with the part-of-speech information learned from constructed lexical relations to enrich span representation. We conduct extensive experiments on benchmark datasets to evaluate the efficacy and efficiency of the proposed method. The experimental results show that model Dual-Span out- performs all state-of-the-ard art methods on the ASTE task.
ef-
Second, we also observe that there are some frequent patterns in aspect and opinion spans in terms of pfrt-of-speech. For instance, in many cases aspect terms are noun or noun phrase which we refer to as N N and (NN NN.. respectively. Moreover, it is fairly common that opinion terms are adjective (denoted by J.J). As shown in Fig- ure 1, the aspect "vegetable risotto" has the part-of- speech structure NNN NN,, and opinion term "burnt" is JJ. Therefore, it is possible to extract the aspect/opinion spans according to the lexical avoid enumer-
2 Related Work
Aspect-based sentiment analysis (ABSA) (Pontiki et al., 2014; Schouten and Frasincar, 2016; Xue and Li, 2018; Chen et al., 2022a; Trusca and Frasincar, 2023) is fine- ame-ggained sentiment analysis. The early work of ABSA was to identify its three sentiment elements (i.c., aspect, opinion, sentiment polarity) as basic tasks: ATE (e.g., (Yin et al., 2016; Ma et al., 2019; Chen and Qian, 2020; Li et al., 2020). OTE (Yang and Cardie, 2012, 2013; Wan et al., 2020)) and ASC (c.g., (Wang et al., 2016; Tang et al., 2016; Xue and Li, 2018; 88;; Du et al., 2019;
characteristics of the words ating all word combination.
Motivated by the two observations, propose dual-channel span generation approach for aspect- level sentiment triplet extraction, which we term as Dual-Span. Dual-Span utilizes two relational

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Span
Symtectic
My
veggetable
Syntactic Dependency
SynGAT
Word Indasiting
My
My vegetable sientto
Aspect
risotto was
vegetable
dootto
Sp
DEN mUn REERS aptrdh
Baver
Saftmoo
and
BILSTM /BERT
Decinel
infused
Span
totally
begebable
in
Pbotto
Part-pf- Speach
Relation
PosGAT
Mac
Bavor
Opinion Candidatos
Tridke Set
burn
repetablic rborto
flavor
Part
Syntactic Dependency Type:
Purt-at-Sppechh Relation:
My septastl rostle
Ieayed Taver
Figure 2: The overall architecture of Dual-Span.
et al., 2021; Brauwers and Frasincar, 2023)). Subsequently, some studies began to consider mul- tiple sentiment element composite tasks in order to better understand fine-graines sentiment analysis: aspect term polarity co-extraction (APCE) (Li and Lu, 2017: He et al., 2019; Li al., Aspect- Opinion Pair Extraction (AOPE) (Zhao al., 2020; Wu et al., 2020a; Gao et al., 2021; Chakraborty et al., 2022) and Aspect Category Sentiment Analy- sis (ACSA) (Schmitt et al., 2018; Hu et al., 2019;
the span of sentiment triplets.
3 Proposed Framework
In this section, the overall architecture of our pro- posed model Dual-Span is shown in Figure 2, which consists of four main components: sentence encoding, feature enhancing module, dual-channel span generation and triplet module.
3.1 Task Definition
Cai et al.. 2020; Liu et al., 2021).
For a sentence X = -wi, W2,.... ... Wn) of length n, the ASTE task to extract the set as- pect sentiment triplets T = {(a, o, 8) 1 from the given sentence X, where o and E [POS, N EU, NEG] represent the aspect term, opinion term and sentiment polarity, respectively.
Some recent works started consider the integrity among the three sentiment elements and thus proposed the ASTE task. A diversity techniques were proposed for it: two-stage pipeline (Peng et al., 2020), multi-task unified framework (Li et al., 2019; Zhang et al.. 2020; Yan et al., 2021), multi-round machine reading comprehension method (Mao et al., 2021; Chen al., 2021a; Liu al., 2022) and end-to-end method (Wu al., 2020a: Xu et al., 2020; Chen al., 2021b, 2022c; Xu al., 2021; Chen al., 2022d). The span-level based approaches adopt end-to-end implementation. For instance, Span- ASTE (Xu et al., 2021) enumerates aspect and viewpoint spans and directly exploits their interac- tion to solve ASTE tasks, while SBN (Chen et al., 2022d) proposed span-level bidirectional network that enumerates all possible spans as input, and completes the ASTE task by designing two de- coders and adopting inference strategies. Despite that, it still remains an open challenge to improve the search efficiency and feature representation for
|T| is the number of sentiment triplets contained sentence X.
3.2 Sentence Encoding
To obtain contextual representations for each word, we explore two sentence encoding methods. namely, BiLSTM and BERT.
BiLSTM We first use the GloVe (Pennington et al., 2014) embedding to get the embedding ma- trix E E | of the corpus, where [V] rep- resents the vocabulary size, and d, represents the embedding dimension. For the embedding tokens Fg = {ci, , Cn) in the sentence, we use BiLSTM to get its hidden representation H = (h,, hz,..., h,), where h E IR 2d is obtained by splicing the hidden state E Rd generated by

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forward LSTM and the hidden state h E R gen- erated by backward LSTM:
modify aspect terms, leading to better extraction of aspect-opinion pairs. Finally, for each word's part- of-speech, we add a self-loop relational edge itself, as the diagonal elements shown in Figure 3.
h [h: h]
(1)
BERT An alternative approach is to utilize BERT (Devlin et al., 2019) as the sentence en- coder to generate contextualized word represen- tations. Given a sentence X {wi, 22,, ) with words, the hidden representation sequence H {h1, ...,.... } is the output of the encod- ing layer of BERT the last transformer block.
Iurrt Daver
My vegetable
risstto was burnt
No
DV
repetale
14d
saatto
Ioeal
231
10.37
30.3
3.3 Feature Enhancing Module
Tart-F-peech aljerney evaris
As aforementioned, spans (or intra-span words) in- volve syntactical dependency and part-of-speech correlation, therefore incorporating those informa- tion into feature representations can be beneficial for span pairing and sentiment prediction. To cap- ture the high order dependency relations, here we devise a graph neural network based method to en- code the syntactic dependency and part-of-speech relations of intra- and inter-spans in high orders. In particular, we construct the part-of-speech re- lational graph (corresponding to multi-relation matrix as shown in Figure (b)). Then we apply two relational graph attention networks to learn the high order interactions between words on syntactic dependency tree of the sentence question and constructed pfrt-of-speech graph, respectively.
Figure An example sentence with dependency tree and of adjacency matrices in ASTE task.
On the other hand, the syntactic dependency graph Gym = R$S))) is constructed according to the dependency parsing tree, where edges are represented by syntactic relation types. Morcover, define the self-dependency for each word. So for a given sentence of length n, the syntactic rela- tion between words tc and Wy denoted as R.Smm. whose corresponding vectorization representation is denoted as the vector ri E IRd,, where d, is the dimension of syntactic relation embeddings.
3.3.2 High-order Feature Learning with Relational Graph Attention Network
Next, we use relational graph attention networks (RGAT) to capture the multiple types of linguis- tic features and high-order interaction between spans/words on syntactic dependency graph and part-of- graph, respectively. Moreover, we use two graph attentional network based modules, namely, SynGAT and PosGAT to learn syntactic dependency graphs and part-of-speech graphs, re- spectively, which will distinguish between vari- ous syntactic relationships and part-of-speech re- lationships when calculating the attention weight between nodes. In particular, following previous work (Bai et al., 2021), we denote two specific re- lations on each edge by ri, and rf. respectively. Specifically, for the th node, the update process is as follows:
3.3.1 Part-of-speech Graph Construction
The goal of part-of-speech graph construction is to characterize the word formation patterns of aspect and opinion terms so as to better identify the pos- sible spans. Specifically, we adopt the following three rules to construct the part-of-speech graph PPos_ = (V, (PPe) of given sentence X. First, following previous work (Chakraborty et al., 2022), assuming that aspect terms are usually nouns and opinion terms are usually adjectives, we can define part-of-speech relations based on part-of-speech tags NN or JJ. In particular, we consider the relations between words in a given window that contains words tagged with NN or J. Therefore, a relational edge R of GPos is defined for two words and j as the combination of part-of-speech tags of the two words, whose representation vec- tor is r Rd, where dp is the dimension of pfft-of-speech combination embedding. Besides, we consider the special syntactic relation nsubj. since opinion terms are usually directly used to
(0 12
Σ (1A
ab (winooma (1-00)(11))) 1)- W. Whrt?"))
(2)
(M - 1
Bi (W 17+ 1 Wie (EN())
Σ
(3)

---

where W E R and W p2 x are param- eter matrices. & denotes the number of attention heads, and a is the sigmoid activation. Ng is the set
Finally, we merge the two types of span candi- dates: = [ USK
Compared to exhaustive enumeration on the whole sentence previous mran-based approaches, whose time complexity of enumerated spans is On²)), for sentence of length n. However, in our syntactic span generation, the parsing tree contain- ing 2n edge dependencies (Qi et al., 2020) (includ- ing self-dependent edges), the number of gener- ated spans is ((2)). On the other hand, the statis- tics shows that the benchmark datasets, there are about 2.5 part-of-specch NN and JJ in each sen- tence on average. Therefore, in the part-of-speech span generation procedure, the number of span candidates is 0 (Swindon
(1z) a(lz) of immediate neighbors of node i. dij P.. are the normalized attention coefficients for the 2-th head at the I-th layer.
To fuse syntactic dependency and part-of-speech relation features, we introduce a gating mecha- nism (Cho et al., 2014) to merge the two views follows:
g (Wg [h 1000] + by)
(4)
h = go sm (1 -1-98 g) o hpos
(5)
where is element-wise product operation. [hrem h is the concatenation of hr and hMM
1)) S n, where Swindow is the window size to restrict span length and generally set to be small value (e.g., Swindow 3 in our experiments). That is, the time complexity of our method to generate the span is (n)). which significantly reduce the span candi- date size.
and W and by are model parameters. This way. g is learned to optimize the feature fusion.
3.4 Dual-Channel Span Generation
In this section, we propose dual-channel span generation module, which consists of parts: dual-channel span generation and span classifica- tion.
3.4.2 Span Classification
After obtaining the span candidates S, we fur- ther narrow down the pool of possible spans by leveraging two auxiliary tasks, namely, ATE and OTE tasks. Specifically,
3.4.1 Dual-Channel Span Generation
Syntactic Span Generation Given a sentence X whose syntactic dependency graph is Gyn (V, RSy"), if there is a dependency edge Gi be- tween words wi and Wj. then all words positioned between them considered to be span s In particular, self-dependent edges represents spans of length L, 1. We define the representation of s0m as follows j.j
span candi- dates in will be classified into one of the three categories: (Aspect, Opinion, Invalid] by a span classifier. Next, nz spans are singled out with higher prediction scores 4 Paspect or & opiinion, where is the threshold typer-parameter and & Paspect and d opaiiion obtained by
syn
aaspora (St,) = softmax (FFNN (St,)) (8)
sym Sij
= [1h hy Sundt (i.1)), ef. =1 (6)
opinion (8i,) = softmax (FFNN (8g)) (9)
where (i. denotes trainable embedding of span length ji.e., j - + 1). ei,j = 1 suggests that there is an edge between w; and my..
where FFNN denotes a feed-forward neural net- work with non-linear activation.
Part-of-speech Span Generation For a given sentence X = [wi, .2...... wr). if the part-of- speech tag of word wa is NN or JJ, the words in predefined window will be exhaustively enumer- ated and then the enumeration is further combined with central word w, to form spans. The part-of- speech induced span k can be represented as:
3.5 Triplet Module
Based the shrinked candidate pool of aspect and opinion terms, the aspect candidate 20, E S and opinion candidate shd E S are paired and represented as
pos
Rsa Hs 2 = | Sh abhod faistance (a, b, c.).
pos =(bu: by wwidts (k) 1kg
(10)
(7)
where fdistance (a, b, e, d) denotes trainable em- beddings of span length. embedding vector which is the average pooling
if pos, = NN or JJ, and o c [k;
g raberd is trainable
where Fusdut (k, I) refers to the trainable embed- ding of span length.
of the dependency vectors between words ab

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and ed. Additionally, since opinions are more likely to modify the aspects that match them, we consider the dependency relationship ra od E RSyn between them. Then, sentiment classifi- cation is performed for the obtained span pairs, where the sentiment types are defined as r E (Positive, Negative, Neutral, Invalid) For- mally, the triplct prediction is written as
learning rate of 1e-3 in the training. In the imple- mentation of BERT-based encoding, the model pa-
rameters are optimized using Adamw with a max- imum learning rate of 5e-5 and weight decay of le-2. We run the model for 20 training epochs. For other parameter groups, the same parameter settings are used for both embedding initialization schemes. The maximum span length Lg is fixed to 8, the span pruning threshold is set to 0.5, and the part-of-speech window Swindow is 3. We choose the best model parameters based on the F1 score on the validation set and report the average of the results for different random seeds.
P (r 5 sca) = softmax (PFNN, (Brtwee))) (11)
3.6 Training objective
The loss function for training is defined as the sum of the negative log- ligllikeis from the span-pair classification in the span-classiiicttio and triplets modules:
4.3 Baselines
We compare our model to the following state-of- the-an methods:
Σ log P (fis si.s)
c
including CMLA+ (Wang al., 2017), RINANTE+ (Dai and Song, 2019), Li-unified-R (Li et al., 2019), Peng-two- stage (Peng et al., 2020) and IMN+IOG (Wu et al., 2020b).
Pipeline:
(12)
log P (f sg so.a)
$2,6 s CS
where big and řij are the ground truth labels for span Sij and span-pair (s%, saa): respectively. S. S" and S are the final span representation, pruned aspect and opinion candidate pools dual-channel span generation, respectively.
End-to-end: OTE-MTL (Zhang et al., 2020), JET (Xu et al., 2020), GTS-CNN, GTS- BiLSTM, GTS-BERT (Wu et al., 2020a), S (Chen et al., 2021b). BART-ABSA (Yan al., 2021), MTDTN (Zhao al., 2022), EMC-GCN (Chen al., 2022c). These ap- proaches are end-to-end models that include a unified grid tagging scheme and a position- aware tagging scheme.
4 Experiments
4.1 Datasets
To verify the effectiveness of our proposed model, we conduct experiments on four public datasets, i.e., Lap14, Res14, Res 15 and Res16, which come from the sentiment evaluation benchmarks SemEval 2014 (Pontiki al., 2014), SemEval 2015 (Pontiki et al., 2015) and SemEval 2016 (Pon- tiki et al., 2016), respectively. Moreover, the four datasets have two versions: ASTE-Data-vI(DI for short) (Peng et al., 2020) and (STE-Data-v2(12 for short) (Xu et al., 2020). Statistics for public datasets are shown in the appendix A.1.
MRC: Dual-MRC (Mao et al., 2021), BMRC (Chen et al., 2021a), COM- MRC (Zhai et al., 2022). All these method are based the framework of machine reading comprehension.
Span-based: Span-ASTE (Xu al., 2021), SBN (Chen et al., 2022d). Span-based models consider all possible spans in a sentence and match aspect terms with opinion terms in an end-to-end manner.
4.2 Experimental Setting
4.4 Main Results
We initialize word embedding with two different en- coders: BiLSTM- ----seed and BERT-based encoders. The hidden dimension of BiLSTM-basee encoder is 300 with dropout rate 0.5. To alleviate overfitting, the input embedding dropout rate is 0.7. For the proposed model, we use the Adam optimizer (Loshchilov and Hutter, 2017) with a
We conduct experiments the two versions of four benchmark datasets, i.e., D1 and D2, whose results shown in Table I and Table respec- tively. As can be seen from the two tables, un- der the comprchensive performance indicator FI, the proposed Dual-span consistently outperforms

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Lap14
Res14
Res15
Res16
Category
Model
F1
F1
P
R
F1
Li-umificdR((2019)
42.25
42.78
42.47
41.44
68.79
51.68
43.34
50.73
46.69
38.19
53.47
44.51
Pipeline
1ong-two-stagec22199
48.62
45.52
47.02
58.89
60.41
59.64
51.07
46.04
48.71
59.25
58.09
58.67
BiLSTN
IMG+00220220
49.21
46.23
47.68
59.57
63.88
61.65
55.24
52.33
53.75
GTS (22220
55,93
47.52
51.38
70.79
61.71
65.94
60.09
53.57
56.64
62.63
66.98
64.73
End-to-cad
GTS UTS-BiLSTMI.BR
59,42
45.13
51.30
67.28
61.91
64.49
63.26
50.71
56,29
66.07
65.05
65.56
S 3222))
59,43
46,23
52.01
69.08
64,55
66,74
61.06
56.44
58.66
71 0.00
63. 13
66.87
(
59.85
45.67
$1.80
72.52
62.43
67.08
64.29
52.12
57.56
67. 67.25
61.75
64.37
Ours
Dual Spun
60.14
47.65
53,53
74,82
61.97
68.T9
64.71
57.37
60.76
73.47
62.46
67.49
MTDTN(2022)
61.98
54.71
58.12
70,00
71,78
70.88
59.03
62.68
60.80
69.04
69.98
69.31
End-to-end
BERT
(20222)2
61.46
55.56
58.32
71.85
72.12
71.98
59.89
61.05
60,38
65.08
71.66
68. 18
BMRC(2021)
57.83
70.01
58,74
67.49
MRC-based
(0M-MRC(202))
64.73
56.09
60.09
76.45
69.67
72.89
68.50
59.74
63.65
72.80
70.85
71.79
Ours
Dual-Span
64,50
58.59
61,36
77,55
73.52
75.47
67.66
66.14
66.85
72.44
73.47
72.94
Table Experimental results on dataset D1, including versions of BiLSTM and BERT. All baseline results are from the original paper. Best results are in bold and the second best are underlined.
Lap14
Res
Res15
Res16
Category
Model
P
R
F1
R
F1
P
R
F1
P
R
F1
CMLA+2017)
30.09
36.92
33.16
39.18
47.13
42.79
34.56
39.84
37.01
41.34
42. 12.10
41.72
RINANTE&(2119)
21.71
18.66
20.07
31.42
39.38
34.95
29.88
30.06
29.97
25. 68
22.30
23.87
Pipeline
Li-mmified-B R(2019)
40.56
44.28
42.34
41.04
67.35
$1.00
44.72
51.39
47.82
37.33
54.51
44.31
37.38
50.38
42.87
43.24
63.66
51.46
48.07
57.51
52.32
46.96
64,24
54,21
OTT- (2020)
49.53
39.22
43.42
62.00
55.97
58.71
56.37
40.94
47.13
62.88
52.10
56.96
JET-BERT (,,
55.39
47.33
51.04
70.56
55.94
62.40
64.45
$1,96
57.53
70.42
58.37
63.83
End-to-end
GTS-BERT(2000)
57.52
51.92
54.58
70.92
69.49
70.20
59.29
58.07
58.67
68.58
66.86
67.58
BART-ABSA(2021)
61.41
56.19
58.69
65.52
64.99
65.25
59.14
59.38
59.26
66.60
68.68
67.62
EMC-GCN(2U22)
61.46
55.56
58.32
71.85
72.12
71.98
59.89
61.05
60.38
71.66
68.18
Dual-MRC(2021)
57.39
53.88
55.58
71.55
69.14
70.32
63.78
51.87
57.21
68.60
66.24
67.40
MRC-based
BMRC(202
70.55
48.98
57.82
75.61
61.77
67.99
68.51
53,40
60.02
71.20
61.08
65.75
(OM-MRC(2022)
62.35
58.16
60.17
75.46
68.91
72.01
68.35
61.24
64.53
71.55
71.59
71.57
Span-AST Seon-ASTT-BERRRE 222R(22112)
63.44
55.84
59.38
72.89
70.89
71.85
62.18
64.45
63.27
69.40
71.17
70.26
Spun-based
SBN(2022)
65.68
62.65
76.36
72.43
74.34
69.93
60.41
64.82
71.59
72.57
72.08
Ours(BERRT)
Dual-Span
67.14
62.13
77.01
74.00
75.47
67.97
66.34
67.13
73.56
73.48
73.49
Table 2: Experimental results on dataset D2, all baselines are from the original text. Best results are in bold and the second best are underlined.
all baselines both for BiLTSM encoder and BERT encoder. Moreover, our model achieves the supe- rior performance in precision and/or recall in most
Model
Lap14
Res
Res15
1es16
w/o Dual-RGAT
59.95
69.77
63.4
70.12
w/o SynGAT
61.62
70.12
63.02
70.13
w/o PosGAT
64.23
71.32
62.42
70.89
cases. On the other side, the experimental results suggest that non-pipeline methods (i.e., End-to-end, MRC-based, Span-based) are better than pipeline methods, which should be attributed to the fact that the pipeline methods do not consider the correla- tion between sentiment elements, thus leading to eπror propagation between stages. It is notewor- thy that among tagging based end-to-end methods, some methods that employ syntactic structure of the sentence such as S3E2, MTDTN and EMC- GCN generally outperform the methods that only learn tagging information (e.g., OTE-MTL, GTS and JET), suggesting that the syntactic features of sentences are meaningful for triplet representation. In particular, our end-to-end Dual-Span model out- performs all end-to-end based methods and span- based methods ASTE, SBN, which can be attributed to the fact that our method not only uti-
Transformer
63.16
72.14
63.32
72.49
Dual-GAT
61.71
72.67
64.23
72.13
w/o SynSpan
62.77
73.27
63.86
70.89
w/o PosSpan
64.39
73.46
65.51
71.34
Dual-Span
64.49
75.47
67.13
73.49
Table 3: Experimental results of ablation study.
lizes the syntactic relationship and other linguis- tic features of sentences for span representation learning, but reduce the noise for span generation and pairing, which can facilitate valid span pairing. Specifically, the FI score of Dua-Span on datasets DI and D2 outperforms over other state-of-the- models by about 2% on average.

---

Model
ATE
OTE
Model
Lap14
Res14
Res15
Res16
R
F1
R
F1
Span-ASTE
0.8579
1.1131
0.5368
0.6597
GTS-BERT
76.63
82.68
79.53
76.
78.44
77.25
Tap14
Span-ASTR
83.86
82.28
Dual-Span
0.4443
0.5587
0.2472
0.3169
Dual-Span
80.67
87.92
84.14
78.96
84.07
81.44
GTS-BERT
78.12
85.64
81.69
81.12
88.24
84,53
Table 5: Experimental results of time consumption
Res14
Span-ASTE
83.56
87.59
85, .5
82.93
89.67
86,16
(second) to generate spans on the D2 dataset.
Dual-Span
83.19
89:95
86.44
83.38
88,99
10
CTS-BERT
75.13
78.21
82.52
Res15
Span-ASTE
78.97
84.68
81.72
77.36
84.86
80,93
Dual-Spun
81.49
84.16
82.80
78.17
87.25
82.46
on the D2 dataset with Fl metric and the results are shown in the table 4. On ATE task, Dual-Span is consistently superior Span-ASTE and GTS, indi- cating that syntactic and part-of-speech correlation based candidate reduction and representation are effective for aspect term identification. However, on the OTE task, our model is slightly inferior to Span-ASTE on most of the benchmark datasets, which is caused by lower P values. We expect the reason behind lies in the part-of-speech based span generation. In effect, we only consider the spans involving words that are tagged with Jorr NN N. However, opinion terms can be tagged with V VB,, which we do not include. We leave the expansion of more valid part-of-speech spans in the future work.
GTS-BERT
75.06
89.42
81.61
78.99
88.71
83.57
Res16
Span-ASTE
88.50
83. 83,89
90.91
86.54
Dual-Span
78:45
90.24
83,93
82.24
88.26
85.14
Table 4: Experimental results of ATE and OTE tasks on dataset D2.
4.5 Model Analysis
4.5.1 Ablation Study
To further explore the effectiveness of different modules in Dual-Span, periments on the D2 dataset. Table 3 shows the experimental results in terms of Fl scores D2. W/o SynGAT and w/o PosGAT denote the removal of syntactic graph attention network (SynGAT) and part-of- speech graph attention network (PosGAT), respectively, while W/o Dual-RGAT denotes the removal of both SynGAT and PosGAT. We also compare our approach with unitary graph atten- tion networks Dual-GAT that performs attention convolution on syntactic dependency graphs and part-of-speech graphs respectively without distin- guishing edge types. By comparing w/o SynGAT, w/o PosGAT, w/o Dual-RGAT and Dual-Span, we observe that both the dependency relationship and part-of-speech features of the sentence are infor- mative to the representation of spans. In particular, the syntactic structural feature and part-of-speech information can be complementary. This is mani- fested by the outperformance of Dual-RGAT over Transformer and Dual-GAT. When removing the syntactic span generation module (corresponding to w/o SynSpan) or pfrt-of-speech span genera- tion module (corresponding to w/o PosSpan), the performance is also degraded. This observation il- lustrates that span candidate size can be effectively reduced. Overall, each module of our Dual-span contributes the overall performance of the ASTE task.
conduct ablation ex-
In order verify that our proposed dual-channel span generation strategy can noticeably reduce the computational cost of span enumeration, we test the time consumption of Dual-span and Span-ASTE on span enumeration under the same runtime environ- ment. From the results shown in Table 5, we can see that the proposed dual-channel span generation strategy cuts time cost in half.
Conclusion
In this work, we present a Dual-Span model for improving the performance on ASTE task. Based on the observations of syntactic relations and part- of-speech features among spans, we design a dual- channel span generation method refine the span candidate set so as to mitigate the negative impacts of invalid spans. Moreover, we employ relational graph neural networks to capture the high-order in- teractions between possible spans from both views: syntactic dependency relation and part-of-speech relation. Our experimental results demonstrate that the proposed method brings meaningful gains to ASTE as well ATE task, compared to all base- lines. We also note that for OTE task, our method is generally inferior the vanilla span-based method that enumerate all possible spans. The reason may lie in the limited part-oofspeedl relations, which will be considered in the future work.
4.5.2 Effectiveness of Dual-Span in Span Generation
We use two subtasks, namely, ATE and OTE of ABSA, to explore the effectiveness dual-channel span Generation strategy. We evaluate our model

---

Acknowledgements
Zhuang Chen and Tieyun Qian. 2020. Enhancing aspect term extraction with soft prototypes. In EMNLP. pages 2107-21 17. Association for Computational Linguisties.
This work is supported by National Nature Science Foundation (NO.62276099) and SWPU Innovation Foundation (NO.642).
Kyunghyun Cho. Bart van Merrienhoer, Çaglar Gülçehre, Dzmitry Bahdanau. Fethi Bougares, Hol- ger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In EMNLP, pages 1724-1734, ACL.
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---

A Appendix
79
A.1 Dataset statistics
We counted the data of the two versions sepa- rately 2 As shown the Table 6, in addition to the number of sentences and triples in the dataset, the number of words and multi-word spans is also counted. In addition, we perform part-of-speech statistics the D1 and D2 data sets. The results shown in Table The number of parts of speech with JJ or N N accounts for a relatively high pro- portion of the overall A&O,, and the distribution of the rest of the parts of speech is scattered and unrepresentative. As there is a trade-off between prediction accuracy and time consumption, we only consider spans involving words tagged with JJ or NN in Section 4.5.2.
F1
Window
E119
A.2 Hyperparameter analysis
Maximum Span Length
Figure 4 shows the sensitivity analysis of the hyper- parameters Swindows T.g, the D2 dataset. From the figure, we can observe that the effect is the best when the part-of-speech window Swinddww 3. fact, when the part-of-speech window is set to Swindow = 3, it can basically cover all aspect terms and opinion terms whose parts-of-speech are N NN and JJ. When Swindow exceeds 3, more noise and complexity may be introduced. When the hyperpa- rameter L,=8, the performance is the best.
Figure Sensitivity analysis of the hyperparameters Swindow and Ly dataset D2.
Leck
FI(65)
A.3 Impact of SynGAT and PosGAT Layers
To explore the impact of the number of layers of evaluate
layers
SynGAT and PosGAT in Dual-Span, the number of layers of SynGAT and PosGAT on the D2 dataset, where multiple layers indicate that node information can be propagated to higher-order neighbors. As shown in Figure 6, our model achieves the best performance when both SynGAT and PosGAT are two layers. Specifically, on the syntactic dependency tree of sentence, 2-hops helpful for the interaction between aspect and opin- ion terms, while on the part-of-speech graph, 2-hop relations involving NN or JJ are conducive for capturing valid spans. Note that, the performance declines as the Dual-RGAT goes deeper, which may be due to the oversmoothing (Li et al., 2018) of graph neural networks.
Figure 5: Performance v.s the number of layers in graph networks.
A.4 Visualization of Linguistic Correlations
To explore how the syntactic dependency and part- of-speech correlation between words contribute valid span generation, we visualize the attention scores of the syntactic relations and part-of-speech adjacency matrix two RGAT modules, where rows are queries and columns are keys. As shown in the figure 6, the sampled text "Good creative rolls !" (i.e., the fourth example in the section A.5) contains the triplet: ("rolls", "good creative", "pos- itive"). Since our model employs syntactic depen- dency relation to learn representations, and exploits the part-of-speech information around indicative words (c.g., the words with "NN" tag) as well, the inter-span and intra-span relations can be success-
hhttps://github cow com/xuuuluw// Seréval-Triplet--data
httrp:///github.co///uuuuuuu www/xuwuwwwuuuuuoooo
Tratvall-ripplettdttttreeee

---

Lap14
Res14
Res15
Res16
Dataset
#5
#SW
#MW
#T
#SW
#MW
R5
#SW
#MW
#T
#SW
HMW
Train
899
1452
815
1259
2356
1614
742
1038
863
1421
931
490
225
383
213
170
315
194
239
165
74
216
232
116
test
332
547
291
256
493
674
334
325
493
303
190
328
351
174
Train
906
1460
824
636
1266
2338
1586
752
605
1013
678
335
857
1394
918
476
D2
dev
219
346
190
156
310
577
388
189
148
249
165
84
210
339
216
123
test
328
543
291
252
492
994
657
337
322
485
297
188
326
514
344
170
Table Statistics for the two versions of the dataset. and #T represent the number of sentences and the number of triplets, respectively. #SW indicates that the aspect and opinion terms in the triplets are both single-word spans, while #MW indicates that at least one of the aspect or opinion terms are multi-word spans.
Lap14
Res14
Res15
Res16
Dataset
A&O
JJ&NN
Rat
A&O
JJ&NN
Rat
A&O
JJ&NN
Rat
A&0
JJ&NN
Rat
D1
4447
2949
0.66
7350
5944
0.81
3282
2714
0.83
4262
3523
0.83
D2
4698
3113
0.66
7818
6346
0.81
3494
2882
0.82
4494
3707
0.82
Table 7: Aspect and opinion term part-of-speech statistics on public datasets. Among them, A&:O, JJ&NN represent the number of aspect and opinion terms and the number parts of speech are JJ and NN, respectively, and Rat represents their ratio.
Review
Ground-truth
Span-ASTE
Dual-Span
The baterry very longer.
(haterry, longer,
(baterry, longer.
(baterry. longer,
And windows works like charm.
(windows 7, charm, (mobility,
0
(windows 7. charm. P))
1 wanted it for it is mobility and man,this
(mohility, wanted, P),
(mobility, nice, P)
little bad boy is very nice.
(mobility, nice, P)
Good creative rolls!
(rolls, good creative, P)
(creative rolls, good, P)
(rolls, good creative,
was extensive-though
(wine extensive, P),
(wine extensive,
(wine list, extensive, P)
knowledgeable about pairings.
(staff, seem lodowedggaabee...
[staff, (food, tasty,
for years they have put out the lasty,
(food, P),
(food, delicious, P)
most delicious food and kept it that way
(food, delicious, P)
(food, delicious, P)
Table 8: Case study dataset D2.
Review
Ground-truth((aal of speech)
Dual Span(part of speech)
The OS is fast and fluid, everything organized and it 's just beautiful.
fast(JJ), Organized(VBN) Iluid(NN), beautiful(TT) ruined( VBN) (verrated(I)),
fast(JJ),
fluid(NN), beautiful(IT)
This place has ruined me for neighborhood sushi. T think the pizza is so overrated and was under cooked,
0
overrated((J)
under cooked(IN, VBN)
Decor needs to be upgraded but the food is amazing! upgraded(VBBN) amazing(JJ)
amazing(JJ)
Table 9: A case study of prediction emrors in OTE tasks on the D2 dataset.

---

Review
ASrOppurt of spoceh)
Ground-iruth
Dual-Spin
The OS fast and Buid. everything organized
NNNNNP), East)U, BuidNNN),
los, Ease, P% cos, organized, PX.
(os, fase, PJ, (os, fluid, Pl. beautifal,
bcautiful,
oeganized/VBN, beautful(())
beautiful,
This place has naned me for neighhorhood sushi. think the pizza
sashi(NN), Viin
(sushi, ruined. F)
PizzaINN), ovaraleed)),
(uzza, overtaied, N),
(pizes, overrated N)
overrated and was under cooked.
under cooked[LS, VBN)
(pizza, under cooked, N)
decor[NN), food(NN)
(decor, upgiaded, N),
Decor nceds to he upgraded her the food amazing!
(food, amazing.
upgradedVVBN) amazing()I)
amazing.
The manager was nude and
manager[NN) rade(VBN)
(manager, rude, N), (manager, poorly, N), (situation, poorly. N)
handled the extremely poody.
(manager,
Table 10: case study of prediction errors on the D2 dataset.
fully captured by graph attention networks. So the aspect term "rolls" pays attention to opinion terms "good" and "reeativ"" (the last row of the left plot in Figure 6), while the two words with "JJ" tag, i.e., "good" and creative" shows more strong correla- tion than with "rolls" in part-of-speech graph (cor- responding to the right plot of Figure 6), demon- strating that they are more likely to fall into the same category. As result, our model gives the correct triplet, in contrast to Span-ASTE whose prediction ("creative rolls", "good", "positive").
5th, and 6th examples, it can be shown that our method makes full use of syntactic and semantic information to improve the accuracy of effective span capture. can be shown the fourth example that our method reduces the span boundary error by using part-of-speech structural features.
A.6 Error analysis
In order to explore the reason behind the slight in- feriority of our model on the OTE task, compared to Span-ASTE most benchmark datascts, we conduct error case study analysis the datasets of the D2 version. As shown in Table 9, since we do not use the tag "VN" in constructing part- of-speech graph, our method fails to extract the opinion words with the part of speech "V BN" the four examples, e.g. "organized (VBN)" and "upgraded(V BN)". Additionally, to identify the limitations of our work and potential areas for im- perform error sample
Good creative rolls
provement in the future, analysis the D2 dataset. As shown in Table 10, for opinions whose part of speech is not J.J, our method is more likely to give wrong prediction results. Moreover, there are some non-aspect or opinion words whose part-of-speech are NN or JJ, which also mislead the model to make wrong span identification.
Review: "Good creative rolls!"
the most Lasty, most dellcciuss food and kept that
...RP DT
RBS
RBS
UNN CC. VRP DT NN
off
(b) Review: "for 7 years they have put the most tasty, most delicious food and kept it that way..."
Visualization of adjacency tensors of syntactic (left) and opreehh combination features (right).
Figure 6:
A.5 Case Study
We use several examples from the test of dataset D2 analyze and validate our method, shown in the Table 8. For the first example, our method Dual-Span may perform better in predicting sen- timent consistency than Span-ASTE. On the 2nd,


Reviews:
[{"review_id": "eRFGVBUyFK", "reviewer": "Reviewer_vjaB", "summary": "", "questions": "The same to the section \"Reasons To Reject\"", "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": "Comment 1.The motivation of the paper is much unclear. Specifically, what does the “higer-order interaction” refer to? And how does the multiple dependency transmit the higer-order interactions between spans? How does “indirect relation” suggest the relevance of the sentiment polarity between spans? And how to define the “relevance” between spans? Moreover, only one case is given in the paper and how to evaluate the generalization of the observations by the authors?\n\nReply：Thank the reviewer for the comments. “Higher-order interaction” refers to the interaction (i.e., message passing) between two distant words in the syntactic dependency graph via multiple hops. For instance, the direct dependency between words is first-order interaction while other indirect dependencies belong to higher-order interaction. So it is clear that higher-order interaction between spans/words will be recursively transmitted by the direct neighbors of the words. The relevance is then reflected by the degree of the interaction between words. Indirect interaction generally suggests some relevance weaker than that of direct interaction. Regarding “how to define the relevance”, the relevance between spans means that there exits syntactic dependency between spans. We have elaborated the above points in the revised manuscript.\n\nComment 2.The content of “Related work”is incomplete. First, there are many repetitions between “Introduction” and “Related work”. Second, a conclusion of the difference and advantages in this work should be represent clearly compared with prior work in Section 2“Related work”. Third, the“span-level” ASTE task should be detailed introduction in “Related work”since this work focus on alleviating the span-level ASTE noise problem.\n\nReply：We agree that there are some repetitions between introduction and related work, since the introduction summarizes the existing work and their core ideas, which have been repeated in the “related work”. We demonstrate the difference and advantages of this work compared with prior work in the “Introduction”, not in the “related work”. We appreciate the suggestions from the reviewer for improving the writing.\n\nComment 3.It’s not clear what does the “Dual-GAT” mean? and what is the difference between \"Dual-GAT\" and “Dual-RGAT”. The reason for the replacing “Dual-RGAT” with Transformer is not clear.\n\nReply: We feel sorry for ignoring the explanation of “Dual-GAT”. Compared to Dual-RGAT, Dual-GAT performs graph attentional convolution on syntactic dependency graph and part-of-speech graph respectively without differentiating edge types. As to “Transformer replacing Dual-RGAT”, the reason is that one can evaluate the role of syntaxial relations between words by comparing the performances of specific relation-based learning (i.e., Dual-RGAT based on syntaxial graphs) and complete-graph based learning (i.e. Transformer without considering syntactics and POS) .\n\nComment 4.1 More experimental analysis should be added to evaluate the following points: 1) “However, on the OTE task, our model is slightly inferior to Span-ASTE on most of the benchmark datasets”in Section 4.5.2.\n\nReply: Thank the reviewer for the valuable comments. We have incorporated the error case study in Section 4.5.2 in the revision. As shown in table below, since we do not use the tag “VBN” in constructing part-of-speech graph, our method fails to extract the opinion words with the part of speech \"VBN\" in the four examples, e.g. “organized” (VBN) and “upgraded”(VBN). Accordingly, our method is slightly inferior to Span-ASTE on the OTE task, because we only consider the spans involving words that are tagged with “JJ” or “NN”, while ASTE enumerates all possible spans with the maximum coverage. \n\n| Review | Ground-truth(part of speech)| Dual Span(part of speech)|\n| -------- | -------- | -------- |\n| The OS is fast and fluid, everything is organized and it 's just beautiful. | fast(JJ), fluid(NN), organized(VBN), beautiful(JJ) | fast(JJ), fluid(NN), beautiful(JJ) | \n| This place has ruined me for neighborhood sushi.    | ruined(VBN)     | { }    |\n| I think the pizza is so overrated and was under cooked.    | overrated(JJ),  under cooked(IN, VBN)     | overrated(JJ)    |\n| Decor needs to be upgraded but the food is amazing!    | upgraded(VBN), amazing(JJ)   | amazing(JJ)     |\n\nComment 4.2 More experimental analysis should be added to evaluate the following points: 2) “Effectiveness of Dual-Span in Span Generation” and we only consider the spans involving words that are tagged with \"JJ\" or \"NN\".\n\nReply:As for the choice of “JJ” and “NN” in our experiments, in fact, there is a tradeoff between prediction accuracy and time consumption, that is, more tags means more time. So we statistically analyze the frequencies of different tags in the datasets. As shown in table below, among them, A\\&O, JJ&NN represent the number of aspects and opinion items, respectively, the number of part of speech is \"JJ\" or \"NN\", and Rat represents their ratio. The parts of speech of “JJ” and “NN” account for the majority of aspect and opinion, and the rest of the parts of speech are not representative. This is why we adopt “JJ” and “NN”.\n\n| Dataset  | Lap14 |       |      | Res14 |       |      | Res15 |       |      | Res16 |       |      |\n| -------- | ----- | ----- | ---- | ----- | ----- | ---- | ----- | ----- | ---- | ----- | ----- | ---- |\n|          | A&O   | JJ&NN | Rat  | A&O   | JJ&NN | Rat  | A&O   | JJ&NN | Rat  | A&O   | JJ&NN | Rat  |\n| Dataset1 | 4447  | 2949  | 0.66 | 7350  | 5944  | 0.81 | 3282  | 2714  | 0.83 | 4262  | 3523  | 0.83 |\n| Dataset2 | 4698  | 3113  | 0.66 | 7818  | 6346  | 0.81 | 3494  | 2882  | 0.82 | 4494  | 3707  | 0.82 |\n\nStatement: The code has been uploaded to GitHub", "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.Extensive experiments are conducted and the results can evaluate the effectiveness of the proposed model.\n\n2.The code will be released  after acceptance. ", "reasons_to_reject": "1.The motivation of the paper is much unclear. Specifically, what does  the “higer-order interaction” refer to?  And how does the multiple dependency transmit the higer-order interactions between spans?  How does “indirect relation” suggest the relevance of the sentiment polarity between spans? And how to define the “relevance” between spans? Moreover, only one case is given in the paper and how to evaluate the generalization of the observations  by the authors? \n\n2.The content of “Related work”is incomplete. First, there are many repetitions between “Introduction” and “Related work”. Second, a conclusion of the difference and advantages in this work should be represent clearly compared with prior work in Section 2“Related work”.  Third, the“span-level” ASTE task should be detailed introduction in “Related work”since this work focus on alleviating the span-level ASTE noise problem.\n\n3.It’s not clear what does the “Dual-GAT” mean? and what is the difference between \"Dual-GAT\" and “Dual-RGAT”. The reason for the replacing  “Dual-RGAT” with Transformer is not clear.\n\n4.More experimental analysis should be added to evaluate the following points: 1) “However, on the OTE task, our model is slightly inferior to Span-ASTE on most of the benchmark datasets”in Section 4.5.2 and 2) “Effectiveness of Dual-Span in Span Generation” and “we only consider the spans involving words that are tagged with JJ or NN”.\n\n\n\n", "questions_to_authors": "The same to the section \"Reasons To Reject\"", "paper_topic_and_main_contributions": "This paper propose a dual-channel span generation method to reduce span candidates number for alleviating noise in sentiment triplet extraction in Aspect Sentiment Triplet Extraction. Specifically, this paper utilize two relational graph attention networks(RGAT) to learn high-order syntactic dependency between words/spans and linguistic features among words. Then, fusing the syntactic and lexical information to enrich span representation for effectively reducing the noisy information of valid span candidates.", "excitement": 2, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "None"}, "extra_text": {}}, {"review_id": "yZ6IQ2S7eD", "reviewer": "Reviewer_khna", "summary": "", "questions": "Here are my questions:\n1.Why did you choose to compare the proposed method with only span-based approaches and not with non-span-based approaches, such as sequence labeling or joint modeling? Do you think the proposed method would perform better or worse than these approaches?\n\n 2.Could you provide a detailed analysis of the errors made by the proposed method? What are the limitations and potential areas for improvement of the approach?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Comment 1. Limited evaluation: The proposed method is evaluated on only two public datasets, which may not be sufficient to demonstrate its effectiveness and generalizability to other domains and languages. The authors could consider evaluating the approach on more diverse datasets to address this limitation. \n\nReply: Thank to the reviewer for raising this issue. In the field of aspect-level sentiment analysis, the public datasets that contain parsing tags and triplet labels are quite limited and all included in the benchmarks we use (i.e., Lap14, Res14, Res15, Res16). Due to limited time, we will leave the supplementary experiments in the future work. \n\nComment 2. Lack of comparison with non-span-based approaches: The authors compare the proposed method with existing span-based approaches, but do not compare it with non-span-based approaches, such as sequence labeling or joint modeling. This may limit the scope of the evaluation and the potential impact of the approach. \n\nReply: Thank the reviewer for this comment. In our original manuscript, we have the comparisons between our method and non-span-based approaches (i.e., Table 1 and Table 2 in the original manuscript). Specifically, we compare with two classes of non-span-based approaches: sequence markers (i.e., GTS, JET, S3E2, EMC-GCN), and joint modeling methods (i.e., Li-unified-R, Peng-two-stage, IMN+IOG). The results of these methods are reported in Table 1 and Table 2 in our paper.\n\nComment 3.  Lack of analysis of errors: The authors do not provide a detailed analysis of the errors made by the proposed method, which could help identify the limitations and potential areas for improvement. The authors could consider providing such analysis in future work.\n\nReply: We are grateful to the reviewer for this helpful comment. We agree that analysis of the errors made by our method is helpful, which we also demonstrate in the conclusion section. Here we present some wrong cases to clarify our statements in the manuscript. As shown in the table below, for opinions whose part of speech is not JJ, our method is more likely to give wrong prediction results. Moreover, there are some non-aspect or opinion words whose part-of-speech are NN or JJ, which also mislead the model to make wrong span identification.\n\n\n| Review | A&O(part of speech) | Ground-truth |  Dual-Span |\n| -------- | -------- | -------- | -------- |\n| The OS is fast and fluid, everything is organized and it 's just beautiful. | os(NNP), fast(JJ), fluid(NN), organized(VBN), beautiful(JJ) | (os, fast, P), (os, organized, P), (os, fluid, P), (os, beautiful, P) | (os, fast, P), (os, fluid, P), (os, beautiful, P) |\n| This place has ruined me for neighborhood sushi.    | sushi(NN), ruined(VBN)     | (sushi, ruined, P)      | { }    |\n| I think the pizza is so overrated and was under cooked.    | pizza(NN), overrated(JJ),  under cooked(IN, VBN)     | (pizza, overrated, N),(pizza, under cooked, N)     | (pizza, overrated, N)     |\n| Decor needs to be upgraded but the food is amazing!    | decor(NN), food(NN), upgraded(VBN), amazing(JJ)     | (decor, upgraded, N), (food, amazing, P)     | (food, amazing, P)    |\n| The manager was rude and handled the situation extremely poorly.     | manager(NN), rude(VBN)     | (manager, rude, N)    | (manager, rude, N), (manager, poorly, N),  (situation, poorly, N)     |", "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 strengths of this paper include:\n\n1. Novel approach: The proposed Dual-Channel Span method is a novel approach to ASTE task that reduces the search space of span candidates and improves the performance of sentiment triplet extraction. The approach generates aspect and opinion spans separately and then combines them to form aspect-opinion pairs, which is different from existing span-based approaches.\n\n2. State-of-the-art performance: The approach is evaluated on two public datasets and achieves state-of-the-art performance, demonstrating its effectiveness and superiority over existing span-based approaches. This is a significant contribution to the field of fine-grained aspect-based sentiment analysis.\n\n3. Clear presentation: The paper is well-written and clearly presents the proposed method, the experimental setup, and the results. The authors provide detailed analysis and discussion of the results, which makes it easy for readers to understand the contributions and limitations of the approach.\n", "reasons_to_reject": "1.  Limited evaluation: The proposed method is evaluated on only two public datasets, which may not be sufficient to demonstrate its effectiveness and generalizability to other domains and languages.  The authors could consider evaluating the approach on more diverse datasets to address this limitation.   2.  Lack of comparison with non-span-based approaches: The authors compare the proposed method with existing span-based approaches, but do not compare it with non-span-based approaches, such as sequence labeling or joint modeling.  This may limit the scope of the evaluation and the potential impact of the approach.   3.  Lack of analysis of errors: The authors do not provide a detailed analysis of the errors made by the proposed method, which could help identify the limitations and potential areas for improvement.  The authors could consider providing such analysis in future work.   ", "questions_to_authors": "Here are my questions:\n1.Why did you choose to compare the proposed method with only span-based approaches and not with non-span-based approaches, such as sequence labeling or joint modeling? Do you think the proposed method would perform better or worse than these approaches?\n\n 2.Could you provide a detailed analysis of the errors made by the proposed method? What are the limitations and potential areas for improvement of the approach?", "paper_topic_and_main_contributions": "The main content of the article is the proposal of a dual-channel span generation approach called Dual-Span for aspect-level sentiment triplet extraction. The approach utilizes two relational graph attention networks to learn high-order syntactic dependency between words/spans and linguistic features in constructed part-of-speech relations among words. The article also discusses the lexical characteristics of aspect and opinion terms and how they can be used to extract aspect/opinion spans. \n\nThe main contribution of the article is the proposal of the Dual-Span approach, which achieves state-of-the-art performance on the aspect-level sentiment triplet extraction task. The approach is shown to outperform existing methods that lack a complete consideration of the relationship between the three sentiment elements. The article also provides insights into the lexical characteristics of aspect and opinion terms, which can be useful for future research in this area.", "excitement": 4, "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": "G85bzOkT6x", "reviewer": "Reviewer_V7Ee", "summary": "", "questions": "1. Why do you say in the complexity analysis that spans are composed only of two words?\n2. What is the difference between a syntax tree and part-of-speech tree?\n3. Are the times to construct all spans below 1 second?\n\nThank you for the rebut, which appropriately answers my questions.", "limitations": "", "rating": 4, "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": "Comment 1.Why do you say in the complexity analysis that spans are composed only of two words?\n\nReply: Thank the reviewer for carefully checking our manuscript. Referring to Section 3.4.1 in the manuscript, Formula 7, the representation of span is composed of the beginning and ending words and the distance between them, so only the first and last words are necessary to be predicted. Therefore, time complexity only involves the computation of the beginning and ending words.\n\nComment 2.What is the difference between a syntax tree and part-of-speech tree?\n\nReply: We do not mention “syntax tree” and part-of-speech tree in the text. We use the syntactic dependency tree. In addition, we leverage part-of-speech tags to construct a part-of-speech graph, which is not a “tree”, as there are cycles in this graph. \n\nComment 3. Are the times to construct all spans below 1 second?\n\nReply: Yes. Span generation in our method takes time less than 1 second, and less than that of Span-ASTE by half. \n\nMissing References:\n\nReply: We appreciate the reviewer's suggestions. We have reviewed the recommended papers and included them as references in the “related work” section, and discussed recent trends in the field of sentiment analysis in the revised version.\n\nTypos Grammar Style And Presentation Improvements:\n\nReply: We have carefully checked the manuscript and corrected all the errors pointed out by the reviewer.", "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": "+ well written paper\n+ novel solution\n+ thorough evaluation", "reasons_to_reject": "- some references missing\n- some explanation missing\n- missing link to code", "questions_to_authors": "1. Why do you say in the complexity analysis that spans are composed only of two words?\n2. What is the difference between a syntax tree and part-of-speech tree?\n3. Are the times to construct all spans below 1 second?\n\nThank you for the rebut, which appropriately answers my questions.", "paper_topic_and_main_contributions": "The paper presents an end-to-end approach for (aspect, opinion, sentiment) triplet extraction based on RGAT and span filtering using two graphs: dependency graph and POS graph. The results show that the proposed approach is superior to existing ones for the triplet extraction task and both graphs complement each other and are useful for the considered task.\n\nThe paper is in general well-written, and the proposed solution seems original. I particularly appreciate the extraction of useful spans that increase the efficiency compared to a brute-force approach. The evaluation is thorough and shows the usefulness of the proposed method.", "excitement": 4, "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": {"Justification_for_Ethical_Concerns": "None."}, "extra_text": {"Missing_References": "-For ABSA:\nKim Schouten, Flavius Frasincar: Survey on Aspect-Level Sentiment Analysis. IEEE Trans. Knowl. Data Eng. 28(3): 813-830 (2016)\n\n-For AD:\nMaria Mihaela Trusca, Flavius Frasincar: Survey on Aspect Detection for Aspect-Based Sentiment Analysis. Artif. Intell. Rev. 56(5): 3797-3846 (2023)\n\n-For ABSC:\nGianni Brauwers, Flavius Frasincar: A Survey on Aspect-Based Sentiment Classification. ACM Comput. Surv. 55(4): 65:1-65:37 (2023)", "Typos_Grammar_Style_and_Presentation_Improvements": "-page 1: \"for\" instead of \"For\" (in title)\n-page 2: “some frequent patterns” instead of “some frequent pattern”\n-page 3: “GloVe” instead of “Glove”\n-page 4: “correlation, incorporating” instead of “correlation. Incorporating”\n-page 4: “each word” instead of “each Word”\n-page 5: “),” instead of “) ,”\n-page 6: “BERT” instead of “bert”"}}]

Author rebuttal:

Meta-review: This paper proposes a dual-channel span generation approach to address the Aspect Sentiment Triplet Extraction (ASTE) task, where two relational graph attention networks are used, namely, syntactic dependency graph and part-of-speech graph. The paper is well written. The solution is reasonable and can help reduce the span candidates. Experiments show the effectiveness of the proposed approach, and can achieve state-of-the-art performance especially on aspect term extraction (ATE) task, while opinion term extraction (OTE) can be improved. The paper uses a lot of acronyms without introduction, e.g., Dual-GAT as pointed out by reviewers. Same for SGAT and PGAT. Please fix all of them by explaining what they mean in the paper. Please add error analysis and improve the related work section. Also please include the responses to reviewers to the paper to make the unclear part clear.
Decision: Accept-Main