Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiments and opinion terms. Previous works based on different modeling paradigms have achieved promising results. However, these methods struggle to comprehensively explore the various specific relations between sentiment elements in multi-view linguistic features, which is the prior indication effect for facilitating sentiment triplets extraction, requiring to align and aggregate them to capture the complementary higher-order interactions. In this paper, we propose Multi-view Linguistic Features Enhancement (MvLFE) to explore the aforementioned prior indication effect in the "Refine, Align, and Aggregate" learning process. Specifically, we first introduce the relational graph attention network to encode the word-pair relations represented by each linguistic feature and refine them to pay more attention to the aspect-opinion pairs. Next, we employ the multi-view contrastive learning to align them at a fine-grained level in the contextual semantic space to maintain semantic consistency. Finally, we utilize the multi-semantic cross attention to capture and aggregate the complementary higher-order interactions between diverse linguistic features to enhance the aspect-opinion relations. Experimental results on several benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.

heading: 1 Introduction
text: Aspect Sentiment Triplet Extraction (ASTE) is a new variant of Aspect-Based Sentiment Analysis (ABSA) (Pontiki et al., 2014), which is an information extraction style task to identify all sentiment triplets from a review to explain WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e., opin-
*Corresponding author.
ion terms). A sentence with triplets marking in Figure 1 illustrates the definition of the ASTE task.
Early work adopts the straightforward solution to decompose the ASTE task into several ABSA subtasks to separately extract sentiment elements of triplets with a two-stage pipeline approach (Peng et al., 2020). To exploit the association among multiple subtasks, Mao et al. (2021); Chen et al. (2021a); Liu et al. (2022) transform the ASTE task into the machine reading comprehension (MRC) paradigm with diverse task-specific queries, which jointly train multiple subtasks but pipeline inference. Obviously, these methods potentially lead to the well-known error propagation problem.
To alleviate this problem, most of recent studies focus on jointly extracting the sentiment triplets in an end-to-end fashion (Xu et al., 2020, 2021; Chen et al., 2022b; Yu et al., 2023). Some efforts formulate the ASTE task as a text generation problem
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to generate the sentiment triplets in one shot (Yan et al., 2021; Gao et al., 2022; Gou et al., 2023). However, these approaches are constrained in capturing reciprocity among the sentiment elements.
Therefore, Jing et al. (2021); Zhang et al. (2022); Liang et al. (2023) construct the word-pair relations by designing a novel grid tagging scheme (Wu et al., 2020a) to capture such association. Chen et al. (2021b); Fei et al. (2022); Chen et al. (2022a) further leverage the syntax dependency or additional linguistic features to enrich the word-pair representations. Nevertheless, these methods primarily fuse the linguistic features and the contextual features at a shallow level, which neglect to align and aggregate them to capture the complementary higher-order interactions. Consequently, they fail to exploit the prior indication effect of multi-view linguistic features for ASTE.
Naturally, two questions arise regarding the prior indication effect by our observations.
Q1: What is the prior indication effect for ASTE? We interpret it as there exist various specific relations between matched aspect-opinion pairs, also within aspect or opinion terms themselves in different views of linguistic features, which facilitate the sentiment triplets extraction. Take Figure 1 as an example. First, in the view of syntactic dependency type, we observe that the aspect term is the nominal subject of the opinion term due to the “nsubj” type. Besides, “cheese” and “pizza” comprise an aspect term by the “compound” type, while “indeed” is the adverbial modifier of “dull” so they combine as an opinion term. Thus, these dependency types facilitate not only the extraction of aspect and opinion terms but also their pairing. Second, in the view of part-of-speech relation, we find that aspect terms are nouns while opinion terms are adjectives. Hence, the word pair with the “NN-JJ” part-of-speech combination tends to form an aspect-opinion pair. In addition, we notice that the matched aspect and opinion terms are closer in terms of the distance of syntactic dependency and relative position.
Q2: How to effectively exploit the prior indication effect to help ASTE? We argue that exploring it requires the "Refine, Align, and Aggregate" learning process. First, there exist numerous irrelevant word-pair relations represented by multi-view linguistic features to ASTE (e.g., relations between commas and other words). Therefore, we need to refine the multi-view linguistic features to focus on the relations of the matched aspect-opinion
pairs. Second, different views of linguistic features represent various relation types for the same aspect-opinion pairs, so they require to be aligned to maintain semantic consistency. Finally, since the prior indication effect of a single linguistic feature is one-sided for ASTE, it is crucial to capture and aggregate the complementary higher-order interactions between diverse linguistic features.
Motivated by the above observations, we propose Multi-view Linguistic Features Enhancement (MvLFE) to explore the prior indication effect to enhance the word-pair relations. Firstly, we introduce the relational graph attention network to encode the word-pair relations represented by each linguistic feature as multi-channel edge features, which are normalized as doubly stochastic matrices to facilitate the aggregation and updating of word nodes, preventing over-smoothing across multi-layer learning. Moreover, edge features are updated by the edge refining strategy which considers the implicit results of aspect and opinion terms extraction to support their matching. Secondly, inspired by Image-Text Matching for cross-modal alignment, we employ the multi-view contrastive learning to align diverse aspect-opinion relations represented by different linguistic features in the semantic space which obtained by the biaffine transformation of contexts to maintain semantic consistency. Finally, we utilize the multi-semantic cross attention to aggregate the complementary higherorder interactions between diverse linguistic features to enhance aspect-opinion relations.
In summary, the key contributions are as follows:
• We propose a novel MvLFE model that effectively exploits the prior indication effect of multi-view linguistic features in the "Refine, Align, and Aggregate" learning process.
• We present the relational graph attention network to refine the relation-aware word-pair representations of multi-view linguistic features to attend the aspect-opinion pairs.
• We employ the multi-view contrastive learning and the multi-semantic cross attention to align and aggregate diverse linguistic features to enhance the aspect-opinion relations.

heading: 2 Related Works
text: Aspect-Based Sentiment Analysis (ABSA) (Pontiki et al., 2014) has received wide attention in recent years. Early works focus on identifying the

heading: Prediction and Triplets Decoding
text: single sentiment element, i.e., Aspect Term Extraction (ATE) (Ma et al., 2019; Li et al., 2020; Wang et al., 2021), Opinion Term Extraction (OTE) (Dai and Song, 2019; Fan et al., 2019; Wu et al., 2020b) and Aspect Sentiment Classification (ASC) (Tang et al., 2022; Ma et al., 2023; Zhang et al., 2023). To further explore the interactions among sentiment elements, some efforts are devoted to coupling the individual subtasks, i.e., Aspect-Opinion Pair Extraction (AOPE) (Zhao et al., 2020; Chen et al., 2020b; Gao et al., 2021) and Aspect Extraction and Sentiment Classification (AESC) (He et al., 2019; Chen and Qian, 2020).
Based on their works, Peng et al. (2020) introduces the more challenging ASTE task to present a near-complete solution for ABSA. Subsequent works address ASTE task by different modeling paradigms: Sequence tagging (Xu et al., 2020; Zhang et al., 2020), Grid tagging (also known as table-filling) (Wu et al., 2020a; Chen et al., 2021b; Jing et al., 2021; Fei et al., 2022; Chen et al., 2022a; Zhao et al., 2022; Zhang et al., 2022; Liang et al., 2023), MRC-based methods with diverse task-specific queries (Mao et al., 2021; Chen et al., 2021a; Liu et al., 2022; Zhai et al., 2022) and Generative methods (Yan et al., 2021; Zhang et al., 2021; Gao et al., 2022; Mao et al., 2022; Lv et al., 2023; Mukherjee et al., 2023; Gou et al., 2023). However, these approaches generally ignore the prior indication effect of multi-view linguistic features for ASTE, which facilitates the triplet extraction.

heading: 3 Methodology
text: In this section, we first introduce the task definition and the adopted tagging scheme. Then we elaborate on the details of our MvLFE model and the training objective. The overall architecture is illustrated in Figure 2.

heading: 3.1 Task Definition and Tagging Scheme
text: Given an input sentence X = {w1, w2, · · · , wn} with n words, the objective of the ASTE task is to extract a sentiment triplets set T = {(a, o, s)k} |T | k=1 from sentence X , where triplet (a, o, s) refers to (aspect term, opinion term, sentiment polarity) and s ∈ {Positive,Neutral,Negative}.
Following by Grid Tagging Scheme (Wu et al., 2020a), we formulate the ASTE task as a unified tagging task by labeling the relations of all word pairs. Take Figure 3 as an example, the left side shows the tagging results for the sentence, while the right side explains the meaning of all tags of word-pair relations we used for ASTE. Specifically, we utilize tags {B-A, I-A, B-O, I-O} to determine the beginning and inside of aspect and opinion terms in the main diagonal of the grid. And we employ tags {A, O} to combine two words that belong to the same aspect term or opinion term in the non-diagonal region of the grid. Meanwhile, the sentiment tags {POS, NEU, NEG} are used not only to match the valid aspect-opinion pairs but also to judge their sentiment polarities.

heading: 3.2 Input and Encoding Layer
text: Pre-trained models have demonstrated their effectiveness in Natural Language Understanding (NLU) tasks (Qiu et al., 2020). In this work we utilize BERT (Devlin et al., 2019) as the context encoder. For a given sentence X = {w1, w2, · · · , wn} with n words, the encoding layer outputs the hidden contextual representationsH = {h1, h2, · · · , hn} at the last Transformer block.

heading: 3.3 Relational Graph Attention Network

heading: 3.3.1 Word-Pair Relation Graph Construction
text: Multi-view linguistic features of a sentence manifest various relations of each word pair. Therefore, it is intuitive to construct each view of linguistic feature as a word-pair relation graph.
Specifically, we utilize off-the-shelf CoreNLP toolkit (Manning et al., 2014) to parse sentences to obtain the different views of linguistic features for word-pair relations, including syntactic dependency type, part-of-speech relation, syntactic dependency distance and relative position distance. For each linguistic feature, we construct it as an undirected heterogeneous graph G = {V, E}, where V and E are sets of nodes and edges. Each word in the sentence is regarded as a node, while word-pair relations represented by the linguistic feature are considered edges. Figure 4 illustrates an example of the word-pair relation graph construction for multi-view linguistic features. For syntactic dependency type, we add a “self ” dependency type to represent self-loop edge for each word node. For part-of-speech relation, we combine the part-of-speech tags as word-pair relation between two words. Besides, we further consider the distances between two words within the syntac-

heading: The The

heading: The The
text: tic dependency tree and the sentence sequence as word-pair relations respectively. We initialize the node features with the hidden contextual representations of words and maintain different trainable embedding look-up tables to represent the edge features of various word-pair relation graphs.

heading: 3.3.2 Linguistic Features Refinement
text: Next, we introduce relational graph attention network (RGAT) to refine the word-pair relations represented by each linguistic feature, denoise the irrelevant word-pair relations to the ASTE and pay more attention to the valid aspect-opinion pairs.
Specifically, for each word-pair relation graph constructed by diverse linguistic features, we denote the node features of wi and wj as hi ∈ Rdh and hj ∈ Rdh respectively, where dh is the dimension of hidden contextual representation. And eij ∈ RP is defined as the relational edge feature represented by the word-pair (wi,wj), where P signifies the channels of edge feature to quantify the association degree of the word-pair relation to each pre-defined tag. Features of each node will be updated by aggregating the features of neighboring nodes and simultaneously incorporating the corresponding edge features. The operation at the l-th RGAT layer is defined as follows:
hli = P
∥ p=1
σ( ∑n
j=1 αlijpW lhl−1j ) (1)
where ∥ denotes the concatenation operator, σ is the ELU activation function, and W l is the weight matrix for the linear transformation of the inputs at the l-th layer. Notably, αlijp denotes the attention coefficients between word nodes hl−1i and h l−1 j on the p-th channel of edge features. For a specific channel of edge features, the attention function is calculated by:
α̂lijp = exp { L(aT [W lhl−1i ||W lhl−1j ]) } el−1ijp (2)
αlijp = DSN(α̂ l ijp) (3)
where L is the LeakyReLU activation function, and aT ∈ R2dh denotes the weight vector. DSN is the doubly stochastic normalization formulated as:
α̃lijp = α̂lijp
Σnt=1α̂ l itp
, αlijp = n∑
t=1
α̃litpα̃ l jtp
Σns=1α̃ l stp
(4)
Unlike the row normalization used in vanilla graph attention network (Velickovic et al., 2018), DSN normalizes the attention coefficients guided by edge features into the doubly stochastic matrices, which possess properties of symmetric, positive semi-definite and the largest eigenvalue 1. These properties contribute to preventing the edge matrix from exploding or shrinking to zero to stabilize the diffusion process, also mitigating the oversmoothing problem across multi-layer RGAT learning (Wang et al., 2018; Chen et al., 2020a).
Subsequently, we update edge features using an edge refining strategy, leveraging the implicit results of aspect and opinion term extraction to refine word-pair relation representations. For example, if wi is an aspect term and wj an opinion term, their pair (wi,wj) is more likely to denote a sentiment relation. The strategy is represented as:
elij = f(e l−1 ij ⊕ el−1ii ⊕ el−1jj ⊕ hli ⊕ hlj) (5)
where f is the multi-layer perceptron with softplus activation function, and ⊕ denotes concatenation.

heading: 3.3.3 Relation Constraint
text: In order to more precisely steer RGAT to refine the word-pair relations, at the last layer, we conduct constraint on the refined word-pair relationsRv = {rv11, rv12, · · · , rvnn} represented by each linguistic feature: Lrc = − ∑
v∈V
n∑
i
n∑
j
∑ c∈C I(yij = c) log(rvij|c) (6)
where V denotes multi-view linguistic features, I(·) is the indicator function, yij is the ground truth of word pair (wi,wj), and C signifies pre-defined tags.

heading: 3.4 Multi-View Contrastive Learning
text: Although different views of linguistic features represent various relation types for the same word pairs, they typically harbor analogous semantic implications, such as jointly reflecting the matching of aspect and opinion terms or the composition of sentiment terms. To ensure that multi-view linguistic features complement and reinforce each other at semantic level, it is vital to align them into the shared semantic space to maintain semantic consistency.
Specifically, we utilize the biaffine mechanism to transform the hidden contextual representations H to construct the semantic space Rba = {rba11, rba12, · · · , rbann} for word-pair relations, due to its proven expressive power in modeling the complex semantics between words (Dozat and Manning, 2017). The process is formulated as:
rbaij = h T i U1hj + (hi ⊕ hj)T U2 + b (7)
where rbaij ∈ RP denotes semantic features of the word pair (wi,wj) with P dimensions. U1, U2 and b are trainable weights and bias.
Inspired by Image-Text Matching for aligning text words and image regions at a fine-grained level (Lee et al., 2018; Diao et al., 2021; Pan et al., 2023), we project each linguistic feature into the semantic space and align them at the level of word-pair relations. First, we compute the cosine similarity matrix for all word pairs between each linguistic featureRv and semantic featureRba, i.e.,
svijk = (rvij) T rbaik ||rvij ||||rbaik || , i, j, k ∈ [1, n] (8)
where svijk denotes the similarity between word pair (wi,wj) in linguistic feature and word pair (wi,wk) in semantic feature. Then we integrate the attended semantic feature to obtain the projection of the linguistic feature in the semantic space:
avij =
n∑
k=1
exp(svijk/τ)∑n m=1 exp(s v ijm/τ) rbaik (9)
where τ is the temperature hyperparameter. Subsequently, we further calculate the cosine similarity between each word pair relation represented by the linguistic feature and its projection in the semantic space to measure the degree of alignment between them, i.e.,
Q(rvij , avij) = (rvij) Tavij ||rvij ||||avij ||
(10)
Finally, we evaluate the overall degree of alignment of the linguistic feature in the semantic space
by LogSumExp pooling, i.e.,
S(Rv,Rba) = 1 λ log
n∑
i=1
exp(λQ(rvij , avij)) (11)
where λ is the scaling factor. We align multi-view linguistic features into the shared semantic space as described above and minimize the hinge-based triplet loss with margin γ as the alignment objective, i.e.,
Lcl = ∑
v∈V [γ−S(Rv,Rba)+S(Rv, R̂ba)]+ (12)
where [x]+ ≡ max(x, 0). We select the linguistic feature Rv and semantic feature Rba of the same sentence as the positive pair, and take the semantic feature R̂ba of other sentences within a mini-batch as negative pairs with linguistic featureRv. In this study, we focus on the hardest negative pair in a mini-batch following by Faghri et al. (2017), which is given by R̂ba = argmaxd̸=RbaS(Rv, d).

heading: 3.5 Multi-Semantic Cross Attention
text: Since the prior indication effect of a single linguistic feature is one-sided for ASTE, such as the nounadjective part-of-speech combination commonly observed in matched or invalid aspect-opinion pairs, it is necessary to capture and aggregate the complementary higher-order interactions between diverse linguistic features to comprehensively enhance representations of word-pair relations.
Specifically, we employ multi-head cross attention (Vaswani et al., 2017) to capture the unique semantic representations of each linguistic feature. For the h-th attention head, we take RQh = WQh Rba, RKh = WKh Rv and RV ah = W V ah Rv as query, key and value. The process is defined as:
Rv→ba = H
∥ h=1 softmax
( RQh (RKh )T√
dk
) RV ah (13)
where dk is the dimension of key. Then we adopt residual connection and layer normalization to aggregate the semantic featureRba and each view’s unique semantic representationsRv→ba, i.e., R̃ba = LayerNorm(Rba + ∑
v∈V Rv→ba) (14)
Finally, we obtain the enhanced representations of word-pair relations Rp = {rp11, rp12, · · · , rpnn} as logits for prediction by:
Rp = fp(R̃ba + ∑
v∈V Rv) (15)
where fp is the fully connected network.

heading: 3.6 Training Objective
text: Our objective is to minimize the training loss as:
L = Lp + βLrc + µLcl (16) where hyperparameters β and µ are for adjusting the impact of corresponding relation constraint loss and contrastive learning loss. The standard crossentropy loss Lp is used for the ASTE task, i.e.,
Lp = − n∑
i
n∑
j
∑ c∈C I(yij = c) log(rpij|c) (17)
The triplets decoding is detailed in Appendix A.1.

heading: 4 Experiments

heading: 4.1 Datasets
text: We evaluate our proposed MvLFE model on four benchmark ASTE datasets (ASTE-Data-V21) released by Xu et al. (2020), which is a refined version of the ASTE-Data-V1 (Peng et al., 2020) to consider cases where one opinion is associated with multiple aspects. Those datasets are derived from Pontiki et al. (2014, 2015, 2016) with one in the laptop domain and three in the restaurant domain. Detailed statistics are shown in Appendix A.2.

heading: 4.2 Baselines
text: We compare our MvLFE model with the following four types of previous state-of-the-art methods: 1) Sequence tagging: Peng-two-stage (Peng et al., 2020), OTE-MTL (Zhang et al., 2020) and JET (Xu et al., 2020). 2) Grid tagging: GTS-BERT (Wu et al., 2020a), EMC-GCN (Chen et al., 2022a) and STAGE-3D (Liang et al., 2023). 3) MRCbased: BMRC (Chen et al., 2021a), COM-MRC (Zhai et al., 2022) and RoBMRC (Liu et al., 2022). 4) Generative: BARTABSA (Yan et al., 2021), LEGO-ABSA (Gao et al., 2022), EHG-Para (Lv et al., 2023) and CONTRASTE (Mukherjee et al., 2023). In addition, we investigate how well can ChatGPT (OpenAI, 2023) solve ASTE task with diverse experimental settings. More details are shown in Appendix B.

heading: 4.3 Implementation Details
text: We adopt the bert-base-uncased2 as the sentence encoder. RGAT is stacked in 3 layers with 300 dimensions of nodes and 10 channels of edges. The hyperparameters τ , λ and γ of multi-view contrastive learning module are set to 0.05, 6 and 0.2.
1https://github.com/xuuuluuu/SemEval-Triplet-data 2https://github.com/huggingface/transformers
For the joint training loss, the ratios β and µ are set to 0.01 and 0.1. During training, AdamW optimizer (Loshchilov and Hutter, 2017) is used with a learning rate of 2e-5 for BERT fine-tuning and 1e-3 for the other trainable parameters. The model is trained for 100 epochs with dropout rate of 0.5 and batch size of 16. For each dataset, we select the model with the best F1 scores on the development set and report the average results of five runs with different random seeds. Our model contains around 112M trainable parameters which are trained on a single NVIDIA A100-PCIE-40GB GPU with CUDA 11.0 and PyTorch 1.7.1. The average runtime for Res15 and other datasets is about 9 and 11 sec/epoch.

heading: 4.4 Main Results
text: The main results are reported in Table 1. Overall, our MvLFE model outperforms all baselines under the F1 metric and achieves superior precision and recall in most cases. The specific observations are that: (1) Methods based on other modeling paradigms outperform sequence tagging models, as the former considers the situation where one aspect or opinion term is associated with multiple opinion or aspect terms. (2) Compared with RoBMRC, MvLFE significantly improves F1 scores by 2.04% on average, overcoming the error propagation problem from pipeline inference in MRC-based models. (3) Our MvLFE model exceeds generative methods by an average of 1.18%~6.96% F1 scores, as the latter overlooks the reciprocity among the sentiment elements. Additionally, experiments on ChatGPT (see more details in Appendix B) reveal its limited

heading: Task Model Res14 Lap14 Res15 Res16
text: ability to capture complex associations between multiple aspect and opinion terms. (4) Our MvLFE also modeled on grid tagging surpasses GTS-BERT and STAGE-3D by an average of 7.82% and 2.08% F1 scores, as it explores the prior indication effect of multi-view linguistic features for ASTE. Note that MvLFE outperforms EMC-GCN by large margins, while EMC-GCN also considers diverse linguistic features. We reckon MvLFE can capture the multi-hop interactions between matched aspect and opinion terms and focus on them with multilayer RGAT but EMC-GCN only considers the one-hop association using a single layer GCN. Furthermore, different from EMC-GCN simply concatenates multi-view linguistic features, MvLFE employs multi-view contrastive learning and multisemantic cross attention to align and aggregate them to maintain semantic consistency and capture the complementary higher-order interactions. More experimental analyses are shown in Appendix A.3.

heading: RGAT

heading: 4.5 Experiments on subtasks
text: To further demonstrate the effectiveness of our MvLFE model, we compare it with three stateof-the-art grid tagging baselines on ATE, OTE and AOPE subtasks. The results are shown in Table 2. Note that our method can directly address these subtasks without additional modifications. Specifically, we observe that our MvLFE model achieves significant improvements over baselines on ATE and OTE task, which suggests that MvLFE can effectively explore the prior indication effect of multiview linguistic features to extract the aspect and opinion terms. In addition, our MvLFE model also exceeds the compared models by a large margin on AOPE task. It further proves that MvLFE can capture reciprocity among the sentiment elements not only for extraction of aspect and opinion terms but also for matching the valid aspect-opinion pairs.

heading: 4.6 Ablation Study
text: To verify the rationality of our MvLFE, we conduct an ablation study with diverse settings and the experimental results are shown in Table 3.
Firstly, we conduct module ablation to investigate the effectiveness of different modules in our MvLFE model. Specifically, w/o RGAT denotes we directly use the initialized multi-view linguistic features for semantic alignment and feature fusion, which slightly decreases F1 scores by 1.34% on average, suggesting RGAT can refine multiview linguistic features to attend matched aspectopinion pairs. w/o MVCL means we remove multi-
view contrastive learning for semantic alignment of multi-view linguistic features, resulting in an average decline of 1.17% on F1 scores, which shows MVCL can align the same word pairs represented by diverse linguistic features to maintain semantic consistency. w/o MSCA indicates we simply concatenate multi-view linguistic features without using multi-semantic cross attention. Thus, it fails to aggregate the complementary higher-order interactions between diverse linguistic features and achieves the dropping performance. Overall, each MvLFE module contributes to the entire performance on ASTE task.
Additionally, we conduct RGAT ablation to interpret intrinsic mechanism of RGAT module. Specifically, w/o DSN means we utilize row normalization instead of doubly stochastic normalization (DSN) to calculate attention coefficients, which decreases F1 scores by 0.35% on average, verifying DSN can mitigate the over-smoothing issue in multi-layer RGAT learning. ERS-concat denotes that we use the node features hi and hj and the corresponding edge feature eij for concatenation as edge feature update and adding for ERS-add. The ablation results show the edge refinement strategy (ERS) improves by 0.21% and 0.27% on average, which indicates the ERS can consider the implicit results of aspect and opinion term extraction with enhancing the semantic connection of edge features eii and ejj . w/o RC signifies that we remove relation constraint loss, and the degraded performance shows that it can precisely steer RGAT to refine the wordpair relations. Overall, it shows the rationality of intrinsic mechanism for RGAT module.
Lastly, we conduct linguistic features ablation to verify the role of each linguistic feature. Specifically, ablating one, two and three linguistic fea-
tures results in an average performance decrease of 1.21%, 2.73% and 4.62% across all datasets, respectively. Completely removing all linguistic features (w/o ALL) causes a more significant performance decline by 7.55% on average. The experimental results suggest that incorporating more linguistic features leads to a more substantial improvement, likely due to the complementary role of linguistic features as the prior indication effect. Overall, it reveals that each linguistic feature is indispensable as the prior indication effect for the ASTE task can be maximized when considering all linguistic features comprehensively.

heading: 4.7 Case Study
text: A case study is given in Figure 5. Specifically, JET only extracts a triplet (cheese pizza, tasty, positive), as it cannot consider the one-to-many case. For ChatGPT, it struggles to extract aspect “cheese pizza” and opinion “indeed dull” that consist of multiple words. Moreover, it incorrectly combines two opinion terms “crispy” and “tasty” into a single opinion term. We reckon ChatGPT fails to understand the composition of sentiment elements and their association, even when given the appropriate prompts. For EMC-GCN, it ignores the triplet (cheese pizza, tasty, positive) as it only captures one-hop neighborhood information, while there are multi-hop connections between aspect “cheese pizza” and opinion “tasty” in syntactic dependency type. In contrast, our MvLFE model can explore the prior indication effect of multi-view linguistic features to precisely extract all sentiment triplets.

heading: 5 Conclusion
text: In this paper, we propose a MvLFE architecture to explore the prior indication effect of multi-view linguistic features for ASTE task in the "Refine, Align, and Aggregate" learning process. We first devise relational graph attention network to encode and refine diverse linguistic features to attend the aspect-opinion pairs. Then we employ multiview contrastive learning for semantic alignment. Moreover, we utilize multi-semantic cross attention to aggregate them to capture the complementary higher-order interactions. Extensive experiments on benchmark datasets show the effectiveness and robustness of our MvLFE model, which consistently outperforms all baselines on ASTE task as well as several subtasks. In the future, we plan to extend our model for information extraction.

heading: Limitations
text: Despite obtaining state-of-the-art performance, our proposed approach still has some following limitations to consider for potential future directions.
• Our MvLFE model is constructed using the grid tagging paradigm. Although grid tagging methods can effectively capture the association among sentiment elements, they commonly suffer from a limitation, i.e., most of the relation labels between two words are irrelevant to sentiment elements (an example shown in Figure 3), which leads to the class imbalance problem of word-pair relations. Thus, when most irrelevant labels are predicted correctly but few for sentimentrelated labels, it causes the phenomenon that the overall loss is low but limited F1 scores of sentiment triplet extraction, which potentially misguides the parameter optimization. Additionally, the requirement to construct a word-pair relations table takes up more memory compared to the models that only consider sequence representations.
• Experiments only verified the consistent improvement on ASTE task, while we intuitively reckon the idea of MvLFE that exploits the prior indication effect of multi-view linguistic features in the "Refine, Align, and Aggregate" learning process can be expanded to some triplet extraction tasks, such as Relation Extraction and Event Extraction.
• The performance of our MvLFE model is still somewhat affected by the parsing quality of the CoreNLP toolkit. The good news is that the CoreNLP toolkit has demonstrated its effectiveness in syntactic dependency analysis and part-of-speech tagging. Thus, despite this limitation, we can still utilize the CoreNLP toolkit to support our research.

heading: Acknowledgments
text: This work was supported by the National Natural Science Foundation of China (No. 62272188) and the Fundamental Research Funds for the Central Universities 2662021JC008. We appreciate the insightful comments and constructive feedback from the anonymous reviewers. We also thank the Huazhong Agricultural University for providing the experimental resources.

heading: A Additional Details

heading: A.1 Triplets Decoding
text: The details of sentiment triplets decoding are shown in Algorithm 1. Specifically, we first obtain the set of diagonal tags D from the prediction results. Each tag in D represents the relation between a word and itself. Then we extract each aspect term or opinion term based on consecutive occurrences of "B-A" and "I-A" labels or "B-O" and "I-O" labels in the diagonal tags set D, thereby forming the aspects set A and the opinions set O. Finally, we identify the sentiment polarities between the matched aspect and opinion terms based on their predicted relations, thus yielding the sentiment triplets set T .

heading: A.2 Dataset Statistics
text: Table 4 shows details about the experiment dataset. We have counted the number of sentences, sentiment triplets for each sentiment polarity, aspect terms and opinion terms within each dataset.

heading: A.3 Additional Experiments and Analyses
text: A.3.1 Visualization of Linguistic Features To intuitively reflect the prior indication effect for the ASTE task, we further visualize word-pair relations represented by multi-view linguistic features after refining with different RGAT layers, by utilizing l2 norm to normalize the edge features into relevance coefficients of the corresponding word pairs.
As shown in Figure 6, the sampled sentence "Tasty food but poor service" contains two sentiment triplets (food, tasty, positive) and (service, poor, negative), we can conclude by specific ob-
Algorithm 1 Triplet Decoding for ASTE Input: The predictions P = {p11, p12, · · · , pnn}
of the sentence X with n words. pij denotes the tag label of the word pair (wi,wj). Output: Triplets set T of the given sentence. 1: Initialize aspects set A = {}, opinions set O = {}, diagonal tags of predictions D = {} and triplets set T = {}.
2: # Get diagonal tags of predictions 3: while i ≤ n do 4: D.append(pii), i← i+ 1 5: end while 6: # Get aspects 7: for pii in D do 8: if pii =="B-A" then 9: while (i+ 1) ≤ n do
10: if pi+1,i+1! ="I-A" then j ← i 11: end if 12: end while 13: A.append([wi, wi+1, · · · , wj ]) 14: end if 15: end for 16: # Get opinions 17: for pii in D do 18: if pii =="B-O" then 19: while (i+ 1) ≤ n do 20: if pi+1,i+1! ="I-O" then j ← i 21: end if 22: end while 23: O.append([wi, wi+1, · · · , wj ]) 24: end if 25: end for 26: # Get sentiment triplets 27: while a ∈ A and o ∈ O do 28: S = {} 29: while wi ∈ a and wj ∈ o do 30: if i < j then tag = pij 31: else tag = pij 32: end if 33: if tag ∈ {POS,NEU,NEG} then 34: S ← S ∪ (tag) 35: end if 36: end while 37: if S ≠ {} then 38: s = argmax(S), T ← T ∪{(a, o, s)} 39: end if 40: end while
servations from the visualization: (1) From the visualization of syntactic dependency type, our MvLFE model pays more attention to word-pair relations with "nsubj" syntactic type across multilayer RGAT refinement, because aspect terms are typically the nominal subject of opinion terms. (2) From the visualization of part-of-speech relation, the correlation coefficients between nouns and adjectives are higher when RGAT deepens, as nouns and adjectives are more likely to form matching aspect-opinion pairs. (3) From the visualization of syntactic dependency distance and relative position distance, the connections between matched aspectopinion pairs are tighter after multi-layer RGAT learning, as valid aspect-opinion pairs are closer in syntactic and relative distance.
In summary, each view of linguistic feature contributes to the unique prior indication effect for the ASTE task, which is indispensable and effective.

heading: A.3.2 Hyperparameters Analysis
text: To investigate the impact of some major hyperparameters, we conduct sensitivity analysis as illustrated in Figure 7.
Specifically, our MvLFE model achieves optimal performance when RGAT is three layers with a range from single to five layers, where multiple layers indicate that MvLFE can capture multihop neighborhood information between words. We reckon that 3-hop interaction between words helps to match the valid aspect and opinion terms with vast distance. Note that the performance declines as the RGAT goes deeper, which may be due to the model matching the invalid aspects and opinions.

heading: Model PLM Res14 Lap14 Res15 Res16
text: For other hyperparameters, margin γ is a threshold that ensures the minimum semantic distance between positive and negative sample pairs to properly align linguistic features with semantic features. β and µ are weights used to adjust relation constraint loss and contrastive learning loss to measure the impact on the training objective. As they gradually increase, the performance initially improves and then decreases, and we finally set γ, β and µ to 0.2, 0.01 and 0.1 as optimal selection.

heading: A.3.3 Experiments on Different Backbones
text: We conduct additional experiments to analyze the sensitivity of our MvLFE model using different pre-trained language models as sentence encoders. The experimental results in Table 5 demonstrate that the performance of our model does not exhibit significant variations when employing different pre-trained language models, which suggests the robustness of our model. To ensure fairness, we adopt BERT as the sentence encoder the same as most of the related studies.

heading: A.3.4 Potential Practical Applications
text: Time complexity: Our MvLFE model is quadratic relative to the input data. The primary source of complexity in this quadratic time complexity is the attention operations within the transformer.
Space complexity: Our MvLFE model takes up an additional parameter space occupation amounts to
2M derived from constructing the word-pair relations table, which is notably minor when compared to the parameter size of the BERT model (110M).

heading: B Experiments with ChatGPT
text: Recently, Large Language Models (LLMs) such as ChatGPT have sparked a revolutionary change in natural language processing technology. They are capable of achieving impressive in-context learning (ICL) (Brown et al., 2020) results with zero-shot and few-shot prompts for unseen tasks, without the need for any parameter updates.
In this paper, we carried out some experiments on four benchmark datasets to investigate how well can ChatGPT solve ASTE task with diverse prompt settings. The experimental results are shown in Table 6. Moreover, we list some examples of prompts for the experimental settings in Table 7. Note that we only list the zero-shot and one-shot prompts due to the limited length of the table. The only difference between few-shot prompts and one-shot prompts is the presence of more data samples.
Specifically, we employ the following experimental settings to verify the performance of ChatGPT on the ASTE task: (1) zero-shot without task instruction, (2) zero-shot with task instruction, (3) one-shot ICL, (4) one-shot ICL with multi-view linguistic features, (5) 3-shot ICL, (6) 3-shot ICL with multi-view linguistic features, (7) 5-shot ICL and (8) 5-shot ICL with multi-view linguistic features. From the experimental results, we first observe that using the task instruction significantly improves the average F1 scores by 7.90%, which suggests it is effective in enabling ChatGPT to understand the purpose of the ASTE task. Second, prompting with training samples further improves the performance. We suppose the reason is that ChatGPT is able to learn from more training samples to extract sentiment triplets from different situations. Note that we also conduct additional experiments for each
ICL setting by incorporating multi-view linguistic features of the given samples. The experimental results show that ChatGPT improves by a large margin with the addition of multi-view linguistic features, suggesting the effectiveness of the prior indication effect of multi-view linguistic features for ASTE.
While ChatGPT has achieved promising results, there is still a gap between ChatGPT and SOTA methods on ASTE task. Based on our observation of the output results, ChatGPT has several limitations to solving ASTE task: (1) It tends to extract incomplete aspect and opinion terms. (2) It often merges multiple juxtaposed opinion terms into a single opinion term. (3) It is prone to underextracting in situations where multiple sentiment triplets exist.

heading: Future Work
text: Despite obtaining state-of-the-art performance, our proposed approach still has some following limitations to consider for potential future directions. • Our MvLFE model is constructed using the grid tagging paradigm. Although grid tagging methods can effectively capture the association among sentiment elements, they commonly suffer from a limitation, i. e. , most of the relation labels between two words are irrelevant to sentiment elements (an example shown in Figure 3), which leads to the class imbalance problem of word-pair relations. Thus, when most irrelevant labels are predicted correctly but few for sentimentrelated labels, it causes the phenomenon that the overall loss is low but limited F1 scores of sentiment triplet extraction, which potentially misguides the parameter optimization. Additionally, the requirement to construct a word-pair relations table takes up more memory compared to the models that only consider sequence representations. • Experiments only verified the consistent improvement on ASTE task, while we intuitively reckon the idea of MvLFE that exploits the prior indication effect of multi-view linguistic features in the "Refine, Align, and Aggregate" learning process can be expanded to some triplet extraction tasks, such as Relation Extraction and Event Extraction. • The performance of our MvLFE model is still somewhat affected by the parsing quality of the CoreNLP toolkit. The good news is that the CoreNLP toolkit has demonstrated its effectiveness in syntactic dependency analysis and part-of-speech tagging. Thus, despite this limitation, we can still utilize the CoreNLP toolkit to support our research