Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task. Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion. However, these methods ignore the relations between words for ASTE task. In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words. Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence. After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively. Thus, relation-aware node representations can be learnt. Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model. Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not. Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly. 1 1 Introduction Aspect Sentiment Triplet Extraction (ASTE) is a new variant of Aspect-based Sentiment Analysis (ABSA). The ASTE task aims to extract aspect sentiment triplets from a sentence, and each triplet contains three elements, namely aspect term, opinion term and their associated sentiment. In Figure 1, an example illustrates the definition of ASTE. To extract the triplets, previous studies have developed three types of approaches. Pipeline approaches (Peng et al., 2020) independently extract elements of the triplet. However, such techniques ignore the interaction between them, and potentially lead to error propagation and extra costs. To utilize the associations among the multiple subtasks, Mao et al. (2021) and Chen et al. (2021a) formulate the ASTE task as a multi-turn machine reading comprehension (MRC) problem and design a model based on BERT to jointly train multiple subtasks. Meanwhile, some efforts devote to extracting the triplets in an end-to-end framework (Xu et al., 2020; Wu et al., 2020a; Zhang et al., 2020; Chen et al., 2021b; Yan et al., 2021), which is constructed mainly by designing new tagging scheme. Although previous works have achieved significant fruits, there exists still several challenges. Here, two questions arise naturally for ASTE task by our observations. 1) How to utilize various relations between words to help ASTE task? Take Figure 1 as an example; for word pair ( gourmet, food ), gourmet and food belong to the same aspect term gourmet food . Likewise, for word pair ( food, delicious ), food is an opinion target of delicious and is endowed with a positive sentiment polarity. Therefore, to 2974 effectively extract the aspect term gourmet food , we expect that gourmet can obtain the information of food and vice versa. To judge the sentiment polarity of the aspect term, information of the opinion term delicious should be delivered to gourmet food . In short, we need to learn task-dependent word representations based on the relations between words. 2) How to utilize the linguistic features to help ASTE task? First, we observe that aspect terms gourmet food and ser-vice are nouns, while opinion terms delicious and poor are adjectives. Thus, the word pair composed of a noun and an adjective tend to form aspect-opinion pair. Second, from the syntactic dependency tree in Figure 1, different dependency types exist in word pairs. For instance, gourmet and food comprise a compound noun because the dependency type between them is compound , while food is the nominal subject of delicious due to the type nsubj . Thus, these dependency types can help not only the extraction of aspect and opinion terms but also their matching 2 . In addition, we consider the tree-based and relative position distances which describe the relevance of two words. In this paper, we propose a novel architecture, Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN), to answer the aforementioned questions. Firstly , we utilize a biaffine attention module to model the relation probability distribution between words in a sentence and use a vector to represent it. Each dimension in the vector corresponds to a certain relation type. To this end, we can derive a relation adjacency tensor from a sentence. Furthermore, our EMC-GCN transforms the sentence to a multi-channel graph by treating words and the relation adjacency tensor as nodes and edges, respectively. In order to learn precise relation between words, we impose relation constraint on the relation adjacency tensor. Secondly , to exploit linguistic features, including lexical and syntactic information, we obtain the part-of-speech combination, syntactic dependency type, tree-based distance and relative position distance of each word pair in the sentence. Similarly, we respectively transform these features into the edges for the multi-channel graphs to further enhance our model. Although part of linguistic fea-2 Matching of word-pair denotes that given w i and w j which respectively belong to an aspect term and an opinion term, if the aspect term and the opinion term form a triplet, then word-pair ( w i , w j ) matches. tures has been applied in other tasks (Kouloumpis et al., 2011; Sun et al., 2019; Phan and Ogunbona, 2020; Li et al., 2021), to the best of our knowledge, they are rarely used in ASTE task. It is non-trivial to explore various linguistic features, adapt and apply them to ASTE in a novel way. Thirdly , inspired by the classifier chains method (Read et al., 2011) in multi-label classification task, we devise an effective refining strategy. Our strategy considers the implicit results of aspect and opinion extraction for word-pair representation refinement when judging whether word pairs match. Our contributions are highlighted as follows: 1) We propose a novel EMC-GCN model for ASTE task. EMC-GCN exploits the multi-channel graph to encode relations between words. Convolution function over the multi-channel graph is applied to learn relation-aware node representations. 2) We propose a novel way to fully develop linguistic features to enhance our GCN-based model, including the part-of-speech combination, syntactic dependency type, tree-based distance and relative position distance of each word pair in a sentence. 3) We propose an effective refining strategy for refined word-pair representation. It considers the implicit results of aspect and opinion extraction when detecting if word pairs match. 4) We conduct extensive experiments on benchmark datasets. The experimental results show the effectiveness of our EMC-GCN model. Traditional sentiment analysis tasks are sentence-level (Yang and Cardie, 2014; Severyn and Mos-chitti, 2015) or document-level (Dou, 2017; Lyu et al., 2020) oriented. In contrast, Aspect-based Sentiment Analysis (ABSA) is an aspect or entity oriented fine-grained sentiment analysis task. The most three basic subtasks are Aspect Term Extraction (ATE) (Hu and Liu, 2004; Yin et al., 2016; Xu et al., 2018; Ma et al., 2019; Chen and Qian, 2020; Wei et al., 2020), Aspect Sentiment Classification (ASC) (Tang et al., 2016; Ma et al., 2017; Li et al., 2018; Zhang et al., 2019; Wang et al., 2020; Li et al., 2021) and Opinion Term Extraction (OTE) (Yang and Cardie, 2012, 2013; Fan et al., 2019; Wu et al., 2020b). The studies solve these tasks separately and ignore the dependency between these subtasks. Therefore, some efforts devoted to couple the two subtasks and proposed effective models to jointly extract aspect-based pairs. This kind of work mainly has two tasks: Aspect and Opinion Term Co-Extraction (AOTE) (Wang et al., 2016, 2017; Dai and Song, 2019; Wang and Pan, 2019; Chen et al., 2020b; Wu et al., 2020a) and Aspect-Sentiment Pair Extraction (ASPE) (Ma et al., 2018; Li et al., 2019a,b; He et al., 2019). Most recently, Peng et al. (2020) first proposed the ASTE task and developed a two-stage pipeline framework to couple together aspect extraction, aspect sentiment classification and opinion extraction. To further explore this task, (Mao et al., 2021; Chen et al., 2021a) transformed ASTE to a machine reading comprehension problem and utilized the shared BERT encoder to obatin the triplets after multiple stages decoding. Another line of research focuses on designing a new tagging scheme that makes the model can extract the triplets in an end-to-end fashion (Xu et al., 2020; Wu et al., 2020a; Zhang et al., 2020; Xu et al., 2021; Yan et al., 2021). For instance, Xu et al. (2020) proposed a position-aware tagging scheme, which solves the limitations related to existing works by enriching the expressiveness of labels. Wu et al. (2020a) proposed a grid tagging scheme, similar to table filling (Miwa and Sasaki, 2014; Gupta et al., 2016), to solve this task in an end-to-end manner. Yan et al. (2021) converted ASTE task into a generative formulation. However, these approaches generally ignore the relations between words and linguistic features which effectively promote the triplet extraction. In this section, we elaborate on the details of EMC-GCN. The overview of the EMC-GCN framework # Relation Meaning 1 B-A beginning of aspect term. Given an input sentence X = { w 1 , w 2 ,    , w n } with n words, the goal of our model is to output a set of triplets T = { ( a, o, s ) m } |T | m =1 from the sentence X , where a and o denote aspect term and opinion term, respectively. The sentiment polarity s of the given aspect belongs to a sentiment label set S = { POS , NEU , NEG } . That is, the sentiment label set comprises of three sentiment polarities: positive, neutral and negative. The sentence X has a total number of |T | triplets. We define ten types of relations between words in a sentence for ASTE. These relations are shown in Table 1. Specifically, four relations or labels, { B-A , I-A , B-O , I-O } aim to extract aspect terms and opinion terms. Compared with GTS (Wu et al., 2020a), the relations we defined introduce more accurately boundary information into our model. The 2976 Figure 3: Table filling for triplet extraction in a sentence is illustrated. B and I denote the beginning of and inside of the term respectively, while -A and -O subtags aim to determine the role of the term, i.e., an aspect or an opinion. The A and O relations in Table 1 are used to detect whether the word pair formed by two different words belongs to the same aspect or opinion term, respectively. The goal of the three sentiment relations { POS , NEU , NEG } is not only to detect whether a word-pair matches or not, but also judge the sentiment polarity of the aspect-opinion pair. Thus, we can construct a relation table for each labelled sentence with table filling method (Miwa and Sasaki, 2014; Gupta et al., 2016). In Figure 3, we show the word pairs and their relations in an example sentence. Here, each cell corresponds to a word pair with a relation. The decoding details of the ASTE task are shown in Algorithm 1. For simplicity, we use the upper triangular table to decode triplets. Firstly, we use the predicted relations of all word pairs ( w i , w i ) only based on the main diagonal, to extract aspect terms and opinion terms. Secondly, we need to judge whether the extracted aspect terms and opinion terms match. Particularly, for an aspect term a and an opinion term o , we count predicted relations of all word pairs ( w i , w j ), where w i  a and w j  o . If there exists any sentiment relation in predicted relations, the aspect term and the opinion term are considered to be paired, otherwise these two are not paired. Finally, for judging the sentiment polarity of the aspect-opinion pair, the most predicted Algorithm 1 Triplet Decoding for ASTE Input: The predicted results P of a sentence X with length n . BERT (Devlin et al., 2019) has demonstrated its effectiveness in various tasks. We utilize BERT as the sentence encoder to extract hidden contextual representations. Given an input sentence X = { w 1 , w 2 , ..., w n } with n tokens, the encoding layer outputs the hidden representation sequence H = { h 1 , h 2 , ..., h n } at the last Transformer block. We utilize a biaffine attention module to capture the relation probability distribution of each word pair in a sentence, since the biaffine attention has been proven effective in syntactic dependency parsing (Dozat and Manning, 2017). The biaffine attention process is formulated as, h ai = MLP a ( h i ) (1) h oj = MLP o ( h j ) (2) g i,j = h ai TU 1 h oj + U 2 (cid:0) h ai  h oj (cid:1) + b (3) r i,j,k = exp ( g i,j,k ) (cid:80) ml =1 exp ( g i,j,l ) (4) R = Biaffine (MLP a ( H ) , MLP o ( H )) (5) where multi-layer perceptron is used. The score vector r i,j  R 1  m models relations between w i and w j , m is the number of relation types and r i,j,k denotes the score of the k -th relation type for word pair ( w i , w j ). The adjacency tensor R  R n  n  m 2977 models relations between words, and each channel corresponds to a relation type. U 1 , U 2 and b are trainable weights and bias. denotes concatenation. Eq. (5) collects process of Eqs. (1) to (4). Motivated by CNN, GCN is an efficient CNN variant that operates directly on graphs (Kipf and Welling, 2017). A graph contains nodes and edges and GCN applies the convolution operation on those nodes connected directly by edges to aggregate relevant information. Given a sentence with n words, the general approach is to use the syntactic dependency tree to construct an adjacency matrix A  R n  n representing a graph for the sentence (Zhang et al., 2019; Sun et al., 2019). The element A ij denotes the edge of node pair ( w i , w j ). Specifically, A ij = 1 if the i -th node is directly connected to the j -th node, and A ij = 0 otherwise. A few studies (Guo et al., 2019; Chen et al., 2020a; Li et al., 2021) construct soft edges by attention mechanism for graph. The edge of any node pair ( w i , w j ) is a probability that indicates the correlation degree between nodes w i and w j . To model various relations between words, our EMC-GCN extend the vanilla GCN with a multichannel adjacency tensor R ba  R n  n  m which is constructed by the aforementioned biaffine attention module. Each channel of the adjacency tensor represents the modeling of a relation between words defined in Table 1. Then, we utilize a GCN to aggregate information along each channel for each node. We formulate the process as follows, (cid:101) H bak =  (cid:16) R ba : , : ,k HW k + b k (cid:17) (6)  H ba = f ( (cid:101) H ba 1 , (cid:101) H ba 2 , ..., (cid:101) H bam ) (7) where R ba : , : ,k  R n  n denotes the k -th channel slice of R ba . W k and b k are the learnable weight and bias. is an activation function (e.g., ReLU). An average pooling function f (  ) is applied over the node hidden representations of all channels. To enhance our EMC-GCN model, we introduce four types of linguistic features for each word pair, shown in Figure 4, including the part-of-speech combination, syntactic dependency type, tree-based distance, and relative position distance. For syntactic dependency type, we add a self dependency type for each word pair ( w i , w i ). In particular, we randomly initialize four adjacency tensors Figure 4: Four types of features for a sentence. based on these features, namely R psc , R dep , R tbd and R rpd . Take syntactic dependency type feature as an example. If a dependency arc exists between w i and w j and the dependency type is nsubj , then R depi,j, : is initialized to the embedding of nsubj by looking up a trainable embedding table; otherwise we initialize R depi,j, : with an m -dimensional zero vector. Subsequently, the graph convolution operation is repeated using these adjacency tensors to obtain node representations  H psc ,  H dep ,  H tbd and  H rpd . Finally, we respectively apply the average pooling function and concatenation operation to all node representations and all edges formally as, H = f (cid:16)  H ba ,  H psc ,  H dep ,  H tbd ,  H rpd (cid:17) (8) R = R ba  R psc  R dep  R tbd  R rpd (9) where H = { h 1 , h 2 , ..., h n } and R = { r 1 , 1 , r 1 , 2 , ..., r n,n } denote node representations and edge representations of word pairs. In order to precisely capture the relations between words, we impose a constraint on the adjacent tensor obtained from biaffine module, i.e., the relation set. Likewise, we impose the relation constraint on four adjacent tensors produced by linguistic features. The constraint costs denote as L psc , L dep , L tbd and L rpd . 3.4.6 Refining Strategy and Prediction Layer To obtain the representation of word pair ( w i , w j ) for label prediction, we concatenate their node representations h i , h j and their edge representation r ij . Moreover, motivated by the classifier chains (Read et al., 2011) method in multi-label classification task, we devise an effective refining strategy, which consider the implicit results of aspect and opinion extraction when judging whether word pairs match. Specifically, assuming that w i is a word in an aspect term and w j is a word in an opinion term, word pair ( w i , w j ) is more likely to be predicted as an sentiment relation, i.e., POS , NEU or NEG . Otherwise, they are unlikely to match. Thus, we introduce the r ii and r jj to refine the representation s ij of word pair ( w i , w j ) , i.e., s ij = h i  h j  r ij  r ii  r jj (11) Finally, we feed the word pair representation s ij into a linear layer, followed by a softmax function to produce a label probability distribution p ij , i.e., p ij = softmax( W p s ij + b p ) (12) where W p and b p are the learnable weight and bias. Our goal is to minimize the objective function as, L = L p +  L ba +  ( L psc + L dep + L tbd + L rpd ) (13) where coefficients  and  are for adjusting the influence of corresponding relation constraint loss. The standard cross-entropy loss L p is used for the ASTE task, i.e., L p =  n (cid:88) i n (cid:88) j (cid:88) c C I ( y ij = c ) log( p i,j | c ) . We evaluate our method on two ABSA datasets. Both of them are from the SemEval ABSA Challenges (Pontiki et al., 2014, 2015, 2016). The first dataset D 1 3 comes from Wu et al. (2020a). The 3 https://github.com/NJUNLP/GTS Dataset 14res 14lap 15res 16res #S #T #S #T #S #T #S #T D 1 train 1,259 2,356 899 1,452 603 1,038 863 1,421 dev 315 580 225 383 151 239 216 348 test 493 1,008 332 547 325 493 328 525 D 2 train 1266 2338 906 1460 605 1013 857 1394 dev 310 577 219 346 148 249 210 339 test 492 994 328 543 322 485 326 514 Table 2: Statistics for two groups of experiment datasets. second dataset D 2 4 is annotated by Xu et al. (2020), which is a corrected version of dataset proposed by Peng et al. (2020). Statistics for these two groups of datasets are shown in Table 2. 4.2 Baselines We compare our EMC-GCN with state-of-the-art baselines. These models are briefly grouped into three categories. 1) Pipeline methods: CMLA+, RINANTE+, Li-unified-R, and Peng-two-stage are proposed by Peng et al. (2020). Peng-two-stage+IOG and IMN+IOG are proposed by Wu et al. (2020a). 2) End-to-end methods: GTS-CNN, GTS-BiLSTM, GTS-BERT (Wu et al., 2020a), OTE-MTL (Zhang et al., 2020), JET-BERT (Xu et al., 2020), S 3 E 2 (Chen et al., 2021b) and BART-ABSA (Yan et al., 2021). 3) MRC-based methods: BMRC (Chen et al., 2021a) is a multi-turn MRC-based model, which is end-to-end in the training phase, but works in pipeline during the inference phase. We use the BERT-base-uncased version 5 as our sentence encoder. AdamW optimizer (Loshchilov and Hutter, 2018) is used with a learning rate of 2  10  5 for BERT fine-tuning and a learning rate of 10  3 for the other trainable parameters. The dropout rate is set to 0.5. The hidden state dimensionality of BERT and GCN are set to 768 and 300, respectively. The EMC-GCN model is trained in 100 epochs with a batch size of 16. To control the influence of relation constraint, we set the hyperpa-rameter  and  to 0.1 and 0.01, respectively. Note that the number of channels equals to the number of relations we defined, which is immutable due to the relation constraint we proposed. All sentences are parsed by Stanza (Qi et al., 2020). We save 4 https://github.com/xuuuluuu/ SemEval-Triplet-data/tree/master/ASTE-Data-V2-EMNLP2020 5 https://github.com/huggingface/ transformers 2979 Model 14res 14lap 15res 16res P R F1 P R F1 P R F1 P R F1 Peng-two-stage+IOG 58.89 60.41 59.64 48.62 45.52 47.02 51.70 46.04 48.71 59.25 58.09 58.67 IMN+IOG 59.57 63.88 61.65 49.21 46.23 47.68 55.24 52.33 53.75 --GTS-CNN 70.79 61.71 65.94 55.93 47.52 51.38 60.09 53.57 56.64 62.63 66.98 64.73 GTS-BiLSTM 67.28 61.91 64.49 59.42 45.13 51.30 63.26 50.71 56.29 66.07 65.05 65.56 S 3 E 2 69.08 64.55 66.74 59.43 46.23 52.01 61.06 56.44 58.66 71.08 63.13 66.87 GTS-BERT 70.92 69.49 70.20 57.52 51.92 54.58 59.29 58.07 58.67 68.58 66.60 67.58 BMRC -70.01 -57.83 -58.74 -67.49 Our EMC-GCN 71.85 72.12 71.98 61.46 55.56 58.32 59.89 61.05 60.38 65.08 71.66 68.18 Table 3: Experimental results on D 1 (Wu et al., 2020a). the model parameters according to the best performance of the model on the development set. The reported results are the average on five runs with different random seeds. The main experimental results are reported in Tables 3 and 4. Under the F1 metric, our EMC-GCN model outperforms all pipeline, end-to-end and MRC-based methods on the two groups of datasets. We observe that end-to-end and MRC-based methods achieve more significant improvements than pipeline methods do, as they establish the correlations between these subtasks and alleviate the problem of error propagation by jointly training multiple subtasks. Note that the tagging schemes of OTE-MTL and GTS-BERT are similar to table filling. Compared with GTS-BERT, our EMC-GCN significantly surpasses its performance by an average of 1.96% and 2.61% F1-score on D 1 and D 2 , respectively. This improvement is attributed to that our EMC-GCN can leverage the relations between words and linguistic knowledge for word representation learning. Another finding is that Model 14res 14lap 15res 16res EMC-GCN 71.78 58.81 61.93 68.33 w/o Ten Relations 70.68 57.71 59.85 66.48 w/o Linguistic Features 71.22 58.38 60.62 67.15 w/o Relation Constraint 70.59 57.28 59.83 67.89 w/o Refining Strategy 70.62 56.72 60.23 67.31 Table 5: F1 scores of ablation study on D 2 . those methods with BERT encoder, such as JET-BERT, GTS-BERT and BMRC, generally achieve better performance than other methods with BiL-STM encoder. We suppose the reason is that BERT has been pre-trained on large-scale data and can provide a strong language understanding ability. To investigate the effectiveness of different modules in EMC-GCN, we conduct ablation study on the second dataset D 2 . The experimental results are shown in Table 5. w/o Ten Relations denotes that EMC-GCN uses the same tagging schema as GTS (Wu et al., 2020a) with six labels. Without 2980 Model 14res 14lap POS NEU NEG POS NEU NEGEMC-GCN 74.69 19.65 62.43 67.74 19.14 56.20 w/o Refining Strategy 74.98 17.39 59.87 67.31 16.08 52.74 Table 6: F1 scores of three sentiment relations on D 2 . the four relations { B-A , I-A , B-O , I-O } , EMC-GCN loses boundary information of terms, the performance drops significantly. w/o Linguistic Features means that we remove the four types of features from EMC-GCN. Without the enhancement of linguistic features, the performance of our EMC-GCN is slightly degraded on 14res and 14lap, but decreased by 1.31% and 1.18% on 15res and 16res, respectively. As 15res and 16res contain less training data, the linguistic features can provide additional information when the training data is insufficient, which is helpful to the prediction of the model. w/o Relation Constraint indicates that we remove the relation constraint loss between the adjacency tensor R ba and the golden label. Thus, each channel in the adjacency tensor cannot precisely describe the relation dependency between words. As a result, the performance of EMC-GCN w/o Relation Constraint on four sub datasets is significantly dropped. w/o Refining Strategy denotes that we remove the implicit results of aspect and opinion extraction r ii and r jj from word pair representation s ij . Since the adjacency tensor has a relation constraint with the golden label, we can suppose r ii as a predicted label or relation probability distribution of word pair ( w i , w i ) on the main diagonal. Thus, we leverage the aspect and opinion extraction implicit results as prior information to help predict the label of word pair ( w i , w j ) . To sum up, each module of our EMC-GCN contributes to the entire performance on the ASTE task. The purpose of refining strategy is to facilitate the word pair matching process based on the aspect and opinion extraction implicit results. To verify the idea, we conduct comparative experiments of three sentiment relations { POS , NEU , NEG } on 14rest and 14lap of D 2 . The results of are shown in Table 6. Note that the function of the three sentiment relations is to detect whether a word-pair matches or not and identify the sentiment polarity of the aspect-opinion pair. The results show that the performance of w/o Refining Strategy has declined markedly and the refinement strategy works as we Figure 5: Visualization of POS and NEG relation channels of adjacency tensor R ba obtained from the biaffine attention. To investigate the effect of relations between words, we visualize the channel slice of adjacency tensor R ba corresponding to a specific relation. Consider the sample sentence, air has higher resolution but the fonts are small. from 14lap dataset. This sentence comprises two triplets, { ( resolution , higher , POS ) , ( fonts , small , NEG ) } . As shown in the left of Figure 5, the visualized adjacency information of higher and resolution corresponds to the POS relation channel. In the visualization, higher and resolution are highly related to each other. As a result, they convey their own information to each other. Similarly, in the right of Figure 5, fonts can receive the node representation and negative sentiment of small in the NEG relation channel. Meanwhile, small can also obtain the information of the opinion target it describes. Thus, our EMC-GCN model can readily predict the correct labels of word pairs ( fonts , small ) and ( resolution , higher ). To further analyze the role of linguistic features on ASTE task, we visualize adjacency tensors of four linguistic features. We use the l 2 norm of feature vector in the adjacency tensor to represent the relevance score of the corresponding word pair. In Figure 6, the first one is visualization of adjacency tensor R psc from part-of-speech combination feature and we observe that the score between adjective and noun is higher, because adjective and noun easily form an aspect-opinion pair, while the score between adjectives is lower, since the two adjectives are usually not related and are likely to be bring noise to each other. In visualization of R dep , we find that each word only has a score with the words it directly depends on, and computes different relevance scores according to different syntactic dependency types. The visualization of R tbd shows that the relevance score calculated for each word with other words at different tree-based distances. The visualization of R rpd demonstrates that the relevance of two adjacent words is greater than that of long-distance word pairs. In summary, all linguistic features we devised contribute to ASTE task. A case study is given in Figure 7. In this example, the aspect terms and opinion terms are highlighted in blue and yellow, respectively. The red line indicates the aspect term and opinion term match, and form a triplet with positive sentiment. The golden opinion term light is hard to identify by GTS-BERT and BMRC, while easy is predicted correctly by all methods, since light is farther from transport than easy . Thus, they ignore the triplet ( transport , light , positive ), while our EMC-GCN can precisely extract it. We argue the key factor is that light and transport can establish significant connections through sentiment relation and linguistic features. In this paper, we propose an EMC-GCN architecture for ASTE task. To exploit relations between words, we first devise a multi-channel graph structure for modeling different relation type of each word pair. Then, we utilize graph convolution operation over all channels to learn relation-aware node representations. Furthermore, we consider linguistic features to enhance the GCN-based model. Finally, we design an effective refining strategy on EMC-GCN for better extracting triplets. Extensive experiments on benchmark datasets show that our EMC-GCN model consistently outperforms all baselines. In the future, we will analyse roles of linguistic features and effects of their combinations. This work was supported in part by the National Key R&D Program of China under Grant 2019YFF0303300 and Subject II under Grant 2019YFF0303302, in part by the National Natural Science Foundation of China under Grants 61906018 and 62076032, in part by the 111 Project under Grant B08004, and in part by the Fundamental Research Funds for the Central Universities under Grant 2021RC36. We appreciate constructive feedback from the anonymous reviewers.
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