SlowGuess/NAACL_2021_OCR huggingface dataset:
Yuanhe Tian\*, Guimin Chen\*, Yan Song\*
$\checkmark$ University of Washington  $\checkmark$ Shenzhen Research Institute of Big Data
$\spadesuit$ The Chinese University of Hong Kong (Shenzhen)
yhtian@uw.edu chenguimin@foxmail.com songyan@cuhk.edu.cn
It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words. However, most of these studies only leverage dependency relations without considering their dependency types, and are limited in lacking efficient mechanisms to distinguish the important relations as well as learn from different layers of graph based models. To address such limitations, in this paper, we propose an approach to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks (T-GCN), where attention is used in T-GCN to distinguish different edges (relations) in the graph and attentive layer ensemble is proposed to comprehensively learn from different layers of T-GCN. The validity and effectiveness of our approach are demonstrated in the experimental results, where state-of-the-art performance is achieved on six English benchmark datasets. Further experiments are conducted to analyze the contributions of each component in our approach and illustrate how different layers in T-GCN help ABSA with quantitative and qualitative analysis. $^{1}$
Aspect-based sentiment analysis (ABSA) processes fine-grained sentiment polarities towards specific aspects, where in many cases, it is required to identify different sentiments for multiple aspects in the same context. For example, in the sentence "The drink menu is limited but the wines are excellent," the sentiment polarity towards "drink menu" is negative while that towards "wines" is positive; an
Figure 1: An example sentence (including the aspect term "drink menu") with its dependency parsing result.
ABSA system may predict wrong if it fails to capture the important contextual information for each aspects. Therefore, to model such contextual information, neural models (e.g., Bi-LSTM and Transformer (Vaswani et al., 2017)) have been widely used for ABSA and demonstrated to be useful for this task (Wang et al., 2016; Tang et al., 2016a; Chen et al., 2017; Ma et al., 2017; Fan et al., 2018).
As a further enhancement of encoding contextual information for ABSA, there are studies (Sun et al., 2019; Huang and Carley, 2019; Zhang et al., 2019a) using graph convolutional networks (GCN) to learn from a graph that is often built over the dependency parsing results of the input texts. As a result, the GCN models are able to learn from distant word-word relations that are more helpful to ABSA. However, GCN models used in these studies are limited by omitting the information carried in dependency types and treating all word-word relations in the graph equally, therefore unimportant relations may not be distinguished and mislead ABSA accordingly. For example, Figure 1 illustrates an example sentence with an aspect highlighted in red, where the aspect word "menu" is connected with three others words, i.e., "the", "drink", and "limited". The connection between "menu" and "limited" could be the most important one since its dependency type, i.e., "nsubj", suggests that "menu" is the nominal subject of "limited", which strongly guides sentiment analysis towards "menu". In this case, if the dependency type is not modeled, one may not be able to leverage such beneficial information. In addition, although previous GCN models learn such
<sup>1</sup>The code and models involved in this paper are released at https://github.com/cuhksz-nlp/ASA-TGCN.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2910-2922, June 6-11, 2021. ©2021 Association for Computational Linguistics
Figure 2: The overall architecture of our approach with an example sentence-aspect pair input (the aspect words "dink menu" are in boldface) from a sentence. Our T-GCN and ALE are marked on top of the figure.
word-word relations by multiple GCN layers, they only use the output from the last layer for ABSA, where the encodings from intermediate layers are omitted and some essential information may be lost because different context information are modeled across layers. Thus an appropriate approach is required to enhance current GCN models for ABSA.
In this paper, we propose a type-aware graph convolutional networks (T-GCN) with multiple layers to enhance ABSA by incorporating both word relations and their dependency types to comprehensively learn from dependency parsing results. Specifically, we firstly obtain the dependency parsing results of the input texts through off-the-shelf toolkits, then build the graph over the dependency tree with each edge labeled by the corresponding dependency type between the two connected words, later apply an attention mechanism to the graph to weight all edges according to their contributions to the task, and finally use attentive layer ensemble to weight and combine the contextual information learned from different GCN layers. In doing so, our proposed T-GCN model can not only model word-word relations and their dependency types, but also distinguish the important contextual information from such relations to enhance ABSA. Experiments on six English benchmark datasets are conducted to evaluate the proposed model, where the results illustrate its effectiveness and state-of-the-art performance is observed over previous studies on all datasets. We also perform further analysis to in-
vestigate the contribution of each component (i.e., type-aware graph, attention for edges, and attentive layer ensemble) in our approach, and illustrate how different layers in T-GCN helps ABSA with quantitative and qualitative studies.
Given an input sentence  $\mathcal{X} = x_{1},x_{2},\dots ,x_{n}$  and the aspect terms  $\mathcal{A}\subset \mathcal{X}$  ( $\mathcal{A}$  is usually a sub-string of  $\mathcal{X}$ ), the conventional ABSA approaches often take the sentence-aspect pair as the input and predicts  $\mathcal{A}$ 's sentiment polarity  $\widehat{y}$  (Tang et al., 2016b; Ma et al., 2017; Xue and Li, 2018; Hazarika et al., 2018; Fan et al., 2018; Huang and Carley, 2018; Tang et al., 2019; Chen and Qian, 2019; Tan et al., 2019; Tang et al., 2020). We follow this paradigm and the overview of our approach is illustrated in Figure 2, with a contextual encoder (i.e., BERT), the proposed T-GCN and the attentive layer ensemble (ALE). The overall conceptual formalism of our approach can be written as
$$
\widehat {y} = \underset {y \in \mathcal {T}} {\arg \max } p (y | A L E (T - G C N (\mathcal {X}, \mathcal {A}))) \tag {1}
$$
where  $\mathcal{T}$  denotes the set of all sentiment labels for  $y$  (i.e., positive, neutral, and negative) and  $p$  computes the probability of predicting  $y \in \mathcal{T}$  given  $\mathcal{X}$  and  $\mathcal{A}$  through T-GCN and ALE. In the following texts, we firstly describe the construction of the graph with dependency types, then elaborate the details of our T-GCN model, and the ALE to incorporate contextual information from different T-GCN layers, and finally illustrate incorporating T-GCN to ABSA.
2.1 Type-aware Graph Construction
Contextual features such as n-grams and syntactic information have been demonstrated to be useful to enhance text representation and thus improve model performance for many NLP tasks (Sun and Xu, 2011; Song and Xia, 2012; Gong et al., 2012; Song et al., 2012; Xu et al., 2015; Chen et al., 2017; Zhang et al., 2019b; Tang et al., 2020). In addition, it is demonstrated by many recent studies that GCN models are effective in capturing contextual features that are represented in graph-like signals, i.e., dependencies among words, of an input sentence (Sun et al., 2019; Huang and Carley, 2019; Zhang et al., 2019a; Tian et al., 2020c; Chen et al., 2020). In the graph for conventional GCN models, each edge between any two words  $x_{i}$  and  $x_{j}$  in the input sentence is added to the graph if there is a
Figure 3: An illustration of how we build the type-aware graph from dependency parsing results and the detail of a T-GCN layer that consumes the graph. Edges and their dependency types are illustrated in the adjacency matrix and the relation matrix, respectively.
dependency relation on them. Therefore, they fail to comprehensively use the dependency parsing results because dependency types are always omitted in the graph. To leverage the such type information, we propose the type-aware graph for feeding our T-GCN via the following steps.
First, we use off-the-shelf toolkits to obtain the dependency results, which can be represented by a list of dependency tuples  $(x_{i},x_{j},r_{i,j})$  with  $r_{i,j}$  denoting the dependency type between  $x_{i}$  and  $x_{j}$ . Second, we use an adjacency matrix  $\mathbf{A} = \{a_{i,j}\}_{n\times n}$  to present the graph by recording word relations in all tuples and a relation type matrix  $\mathbf{R} = \{r_{i,j}\}_{n\times n}$  to represent the edges with their dependency types. Therefore,  $\mathbf{A}$  is a 0-1 matrix where  $a_{i,j} = 1$  if there is an edge between  $x_{i}$  and  $x_{j}$ , and  $a_{i,j} = 0$  otherwise. For  $\mathbf{R}$ , each element  $r_{i,j}$  in it uses a mark to denote the dependency type between  $x_{i}$  and  $x_{j}$ . Figure 3 illustrates the dependency parsing results of an example sentence as well as its type-aware graph represented by  $\mathbf{A}$  and  $\mathbf{R}$ , with the marks for  $r_{i,j}$  listed in the "Type Reference". Finally, to leverage the relation types,
Figure 4: The illustration of how we compute  $\mathbf{h}_i^{(l)}$  for  $x_{3} =$  "menu" through a T-GCN layer. All words  $x_{j}$  connected to "menu" with their dependency types (in embeddings  $\mathbf{e}_{i,j}^{r}$ ) are shown at the bottom part.
we use a transition matrix to map all  $r_{i,j}$  to their embeddings  $\mathbf{e}_{i,j}^{r}$ .
2.2 T-GCN
With the type-aware graph, we propose an  $L$ -layer T-GCN and for each layer we apply attention to the edges in the graph to weight them by their contributions to the ABSA task. Figure 4 illustrates the processes of doing so for the aspect word "menu" in the sentence "The drink menu is limited but all the wines are excellent". In detail, for a each edge between  $x_{i}$  and  $x_{j}$ , the  $l$ -th GCN layer takes the hidden vectors  $\mathbf{h}_i^{(l-1)}$  and  $\mathbf{h}_j^{(l-1)}$  of  $x_{i}$  and  $x_{j}$  from the  $(l-1)$ -th GCN layer ( $\mathbf{h}_i^{(0)}$  and  $\mathbf{h}_i^{(0)}$  are from the context encoder) and concatenate them with the embeddings of their dependency types  $\mathbf{e}_{i,j}^r$  by
$$
\mathbf {s} _ {i} ^ {(l)} = \mathbf {h} _ {i} ^ {(l - 1)} \oplus \mathbf {e} _ {i, j} ^ {r} \tag {2}
$$
$$
\mathbf {s} _ {j} ^ {(l)} = \mathbf {h} _ {j} ^ {(l - 1)} \oplus \mathbf {e} _ {i, j} ^ {r} \tag {3}
$$
Then, we compute the weight  $p_{i,j}^{(l)}$  for this edge by
$$
p _ {i, j} ^ {(l)} = \frac {a _ {i , j} \cdot e x p \left(\mathbf {s} _ {i} ^ {(l)} \cdot \mathbf {s} _ {j} ^ {(l)}\right)}{\sum_ {j = 1} ^ {n} a _ {i , j} \cdot e x p \left(\mathbf {s} _ {i} ^ {(l)} \cdot \mathbf {s} _ {j} ^ {(l)}\right)} \tag {4}
$$
and align the dimension of  $\mathbf{e}_{i,j}^{r}$  to  $\mathbf{h}_j^{(l - 1)}$  by a trainable matrix  $\mathbf{W}_R^{(l)}$  of the  $l$ -th GCN layer by
$$
\mathbf {h} _ {j} ^ {(l - 1) ^ {\prime}} = \mathbf {h} _ {j} ^ {(l - 1)} + \mathbf {W} _ {R} ^ {(l)} \cdot \mathbf {e} _ {i, j} ^ {r} \tag {5}
$$
Finally, we apply  $p_{i,j}^{(l)}$  to this edge and compute the output for  $x_{i}$  at  $l$ -th layer following a similar
process in the conventional GCN by
$$
\mathbf {h} _ {i} ^ {(l)} = \sigma \left(\sum_ {j = 1} ^ {n} p _ {i j} \left(\mathbf {W} ^ {(l)} \cdot \mathbf {h} _ {j} ^ {(l - 1) ^ {\prime}} + \mathbf {b} ^ {(l)}\right)\right) \tag {6}
$$
where  $\mathbf{W}^{(l)}$  and  $\mathbf{b}^{(l)}$  denote trainable parameters in the  $l$ -th GCN layer and  $\sigma$  refers to the ReLU activation function. The above process is conducted for every  $x_{i}$  and throughout all GCN layers, thus the information of dependency types are incorporated into the GCN to enhance ABSA accordingly.
2.3 Attentive Layer Ensemble
For each word  $x_{i}$ , since every T-GCN layer incorporates information from the words that directly connect to it, so that multiple T-GCN layers could learn indirect word relations from long distance. Thus it is assumed that different layers have their unique capabilities to encode contextual information. To utilize such capabilities, we propose to comprehensively learn from all T-GCN layers with attentive layer ensemble.
In doing so, we firstly obtain the output  $\mathbf{o}^{(l)}$  from each T-GCN layer by averaging the output hidden vectors of all aspect terms  $x_{k}\in \mathcal{A}$  ..
$$
\mathbf {o} ^ {(l)} = \frac {1}{| \mathcal {A} |} \cdot \sum_ {x _ {k} \in \mathcal {A}} \mathbf {h} _ {k} ^ {(l)} \tag {7}
$$
where  $|\mathcal{A}|$  is the number of words in the aspect terms  $\mathcal{A}$ . Then we attentively ensemble the output of all T-GCN layers through a weighted average:
$$
\mathbf {o} = \sum_ {l = 1} ^ {L} \delta^ {(l)} \cdot \mathbf {o} ^ {(l)} \tag {8}
$$
where  $\mathbf{o}$  is the final vector output for ABSA and  $\delta^{(l)}$  is a trainable weight assigned to  $\mathbf{o}^{(l)}$  to balance its contribution and satisfying  $\sum_{l=1}^{L} \delta^{(l)} = 1$ .
2.4 Encoding and Decoding with T-GCN
To support applying T-GCN for ABSA, there are necessary encoding and decoding processes. For encoding, there are two ways in doing so. The first is to take the sentence  $\mathcal{X}$  as the input and obtain the hidden vectors  $\mathbf{h}_i^{(0)}$  for all  $x_{i}$  by
$$
\mathbf {H} ^ {\mathcal {X}} = B E R T (\mathcal {X}) \tag {9}
$$
where  $\mathbf{H}^{\mathcal{X}}$  is the hidden vectors of all words in  $\mathcal{X}$  and we use BERT as the encoder (same below). The second is to take the sentence-aspect pair as the input, which can be formalized by
$$
\left[ \mathbf {H} ^ {\mathcal {X}}, \mathbf {H} ^ {\mathcal {A}} \right] = B E R T (\mathcal {X}, \mathcal {A}) \tag {10}
$$
Datasets	Pos. #	Neu. #	Neg. #
LAP14	Train	994	464	870
Test	341	169	128
REST14	Train	2,164	637	807
Test	728	196	182
REST15	Train	907	36	254
Test	326	34	207
REST16	Train	1,229	69	437
Test	469	30	114
TWITTER	Train	1,561	3,127	1,560
Test	173	346	173
MAMS (ATSA)	Train	3,380	5,042	2,764
Dev	403	604	325
Test	400	607	329
Table 1: The number of aspects with positive, neutral, and negative sentiment polarities in all datasets.
where  $\mathbf{H}^{\mathcal{A}}$  is the hidden vectors of all aspect words. Then, the hidden vectors from  $\mathbf{H}^{\mathcal{X}}$  or  $\mathbf{H}^{\mathcal{A}}$  are feed into the T-GCN model as that described in §2.2. For decoding, after we obtain  $\mathbf{o}$  from ALE, we firstly map  $\mathbf{o}$  to the label space by a fully connected layer,  $\mathbf{u} = \mathbf{W} \cdot \mathbf{o} + \mathbf{b}$ , where  $\mathbf{W}$  and  $\mathbf{b}$  are the trainable matrix and the bias, respectively, and each dimension of  $\mathbf{u}$  corresponds to a sentiment type. Thus, we employ a softmax function to  $\mathbf{u}$  and predict the output sentiment  $\hat{y}$  for the aspect  $\mathcal{A}$  in  $\mathcal{X}$  by:
$$
\hat {y} = \arg \max  \frac {\exp \left(u ^ {t}\right)}{\sum_ {t = 1} ^ {| \mathcal {T} |} \exp \left(u ^ {t}\right)} \tag {11}
$$
where  $u^t$  is the value at dimension  $t$  in  $\mathbf{u}$ .
In the experiments, we employ five widely used English benchmark datasets: LAP14 and REST14 from Pontiki et al. (2014), REST15 from Pontiki et al. (2015), REST16 from Pontiki et al. (2016), and TWITTER from Dong et al. (2014), with their official train/test splits. In addition, we try another recently released English dataset, named MAMS $^2$  (Jiang et al., 2019), with the official train/dev/test splits for ABSA, which is much larger than the aforementioned five datasets. It is worth noting that, in addition to the positive, neutral, and negative sentiment labels, LAP14, REST14, and REST16
We use the ATSA part of MAMS obtained from https: //github.com/siat-nlp/MAMS-for-ABSA.
Models	LAP14	REST14	REST15	REST16	TWITTER	MAMS
Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1
BERT-base (S)	77.74	73.30	82.68	73.54	81.34	63.57	88.89	68.19	73.70	71.50	78.94	79.42
+ GCN	79.52	76.01	84.79	77.93	83.60	65.71	90.76	72.79	75.16	72.96	80.69	80.27
+ T-GCN	80.25	76.92	85.54	78.86	85.07	72.50	91.83	76.86	76.16	74.44	81.73	81.12
BERT-base (P)	78.68	74.64	84.55	77.34	83.40	65.28	89.54	70.47	75.00	72.53	80.11	80.34
+ GCN	79.94	76.72	85.09	78.81	84.14	65.75	91.01	73.38	75.29	73.68	81.96	81.31
+ T-GCN	80.88	77.03	86.16	79.95	85.26	71.69	92.32	77.29	76.45	75.25	83.38	82.77
BERT-large (S)	78.06	74.67	83.04	73.27	83.02	68.34	90.20	73.64	73.12	72.08	79.33	79.87
+ GCN	80.09	76.84	86.07	80.35	84.69	70.31	91.48	74.96	75.21	73.69	81.36	81.04
+ T-GCN	81.50	78.48	86.88	81.03	85.07	70.30	92.32	75.83	75.43	73.71	82.70	82.16
BERT-large (P)	79.62	75.77	85.53	77.64	84.14	69.67	91.34	74.35	75.43	73.55	80.62	80.77
+ GCN	80.68	77.85	86.48	80.63	85.42	70.42	91.69	75.24	75.26	73.41	82.56	82.14
+ T-GCN	81.97	78.71	87.41	82.23	86.00	72.81	92.97	80.07	78.03	77.31	83.68	83.07
Table 2: Experimental results (accuracy and F1 scores) of using two encoders i.e., BERT-base and BERT-large, with different configurations on six benchmark datasets. "GCN" refers to the normal GCN model without using type-aware graph, attention mechanism as well as ALE. "S" and "P" refer to the settings that the input is a single sentence and a sentence-aspect pair, respectively.
contain another conflict label, which identifies the aspects that have conflict sentiment polarities. For example, the aspect "sushi" is assigned by a conflict label in "Certainly not the best sushi in New York, however, it is always fresh." from REST14. Therefore, we follow Tang et al. (2016b) to clean the datasets by removing all aspects with the aforementioned conflict label, as well as sentences without an aspect. The statistics (number of aspects with positive, neutral, and negative labels) of the processed six datasets are reported in Table 1.
To build the graph for T-GCN, we firstly use the current best performing constituency parser, i.e.,  $\mathrm{SAPar}^3$  (Tian et al., 2020d), to parse all input text into constituency trees, then convert the trees into dependency trees by Stanford Converter $^4$ , and finally build the graph over the dependency relations and types from the trees. $^5$  Since high quality text representations can improve the performance of NLP models (Mikolov et al., 2013; Song et al., 2017; Bojanowski et al., 2017; Song and Shi, 2018; Song et al., 2018), we employ BERT (Devlin et al., 2019) as the context encoder, which and whose variants (Diao et al., 2020; Dai et al., 2019; Joshi et al., 2020) have demonstrated their effectiveness
to encode context information and achieved state-of-the-art performance in many NLP tasks (Huang and Carley, 2019; Tian et al., 2020a,b; Tang et al., 2020; Nie et al., 2020; Wang et al., 2020). Specifically, we use the uncased BERT-base and BERT-large $^6$  with their default settings, i.e., 12 layers of self-attention with 768 dimensional hidden vectors for BERT-base and 24 layers of self-attention with 1024 dimensional hidden vectors for BERT-large, and use three T-GCN layers. We try two ways to encode the input, where the first encodes the single sentence and the second encodes the sentence-aspect pair. For all models, we use the pre-trained parameters of BERT and initialize all other trainable parameters by Xavier (Glorot and Bengio, 2010). Moreover, we use the cross-entropy loss function for our models and follow previous studies (Tang et al., 2016a; Chen et al., 2017; He et al., 2018a; Sun et al., 2019; Zhang et al., 2019a) to evaluate them via accuracy and macro-averaged F1 scores over all sentiment polarities. For datasets without the official development set, we randomly sample  $10\%$  instances from the training set and regard them as the development set to find the best hyper-parameter setting which is then used to train different models on the entire training set. $^7$
<sup>3</sup>https://github.com/cuhksz-nlp/SAPar  
<sup>4</sup>We use the converter of version 3.3.0 from https://stanfordnlp.github.io/CoreNLP/index.html.  
<sup>5</sup>We also try Stanford CoreNLP Toolkits (https://stanfordnlp.github.io/CoreNLP/) (Manning et al., 2014) and spaCy (https://spacy.io/) dependency parsers with similar results obtained.
<sup>6</sup>We obtain the BERT models from https://github.com/huggingface/pytorch-pretrained-BERT. <sup>7</sup>We report the hyper-parameter settings of different models with their size and running speed in Appendix A and B.
Models	LAP14	REST14	REST15	REST16	Twitter	MAMS
Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1
†Chen et al. (2017)	74.49	71.35	80.23	70.80	-	-	-	-	69.36	67.30	-	-
Ma et al. (2017)	72.10	-	78.60	-	-	-	-	-	-	-	-	-
Fan et al. (2018)	75.39	72.47	81.25	71.94	-	-	-	-	72.54	70.81	-	-
Gu et al. (2018)	74.12	-	81.16	-	-	-	-	-	-	-	-	-
†He et al. (2018a)	72.57	69.13	80.63	71.32	81.67	66.05	64.61	67.45	-	-	-	-
He et al. (2018b)	71.15	67.46	79.11	69.73	81.30	68.74	85.58	69.76	-	-	-	-
Huang and Carley (2018)	70.06	-	79.20	-	-	-	-	-	-	-	-	-
Li et al. (2018)	76.54	71.75	80.69	71.27	-	-	-	-	74.97	73.60	-	-
Chen and Qian (2019)	73.87	70.10	79.55	71.41	-	-	-	-	-	-	-	-
Du et al. (2019)	76.80	73.29	81.79	73.40	-	-	-	-	75.01	73.81	-	-
Hu et al. (2019)	-	-	84.28	74.45	78.58	54.72	-	-	-	-	-	-
*Mao et al. (2019)	75.84	72.49	82.49	72.10	-	-	-	-	72.35	69.45	-	-
*Song et al. (2019)	79.93	76.31	83.12	73.76	-	-	-	-	74.71	73.13	-	-
*Xu et al. (2019)	78.07	75.08	84.95	76.96	-	-	-	-	-	-	-	-
*Jiang et al. (2019)	-	-	85.93	-	-	-	-	-	-	-	83.39	-
†Sun et al. (2019)	77.19	72.99	82.30	74.02	-	-	85.58	69.93	74.66	73.66	-	-
†Zhang et al. (2019a)	75.55	71.05	81.22	72.94	79.89	61.89	88.99	67.48	72.69	70.59	-	-
*†Huang and Carley (2019)	80.10	-	83.00	-	-	-	-	-	-	-	-	-
*†Wang et al. (2020)	78.21	74.07	86.60	81.35	-	-	-	-	76.15	74.88	-	-
*†Tang et al. (2020)	79.8	75.6	86.3	80.0	84.0	71.0	91.9	79.0	77.9	75.4	-	-
*†Our Best Model	81.97	78.71	87.41	82.23	86.00	72.81	92.97	80.07	78.03	77.31	83.68	83.07
Table 3: Performance (accuracy and F1 scores) comparison of our best model (i.e., T-GCN and ALE on large BERT with sentence-aspect pair input) with previous studies on all six benchmark datasets. Models using BERT-large and dependency information are marked by “*” and “†”, respectively.
4.1 Effect of T-GCN
In the main experiments, for each encoder (i.e., BERT base and large), we run two baselines: 1, only using BERT and 2, BERT with normal GCN where all edges are equally treated and the ABSA result is predicted based on the output of the last GCN layer. Table 2 reports the experimental results from all baselines and our models. $^{8}$
There are several observations. First, for both BERT-base and BERT-large encoders, although the models with normal GCN are able to enhance the BERT baselines, our models can further improve the performance in both accuracy and F1 socres on all datasets. This observation clearly illustrates the effectiveness of incorporating dependency type information into GCN and thus improves ABSA accordingly. Second, in most cases, our models that encode the sentence-aspect pair achieve higher results than the ones encoding the single sentence, which is not surprising because the aspect is therefore emphasized in the input and provide more contextual information to be modeled for ABSA.
4.2 Comparison with Previous Studies
To further demonstrate the effective of our approach, we compare the performance of our best