SlowGuess/ACL_2020_OCR huggingface dataset:
Zhuang Chen, Tieyun Qian*
School of Computer Science, Wuhan University, China
{zhchen18，qty}@whu.edu.cn
Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still underexploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is "delicious", the aspect term must be "food" rather than "place". In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task.
Aspect-based sentiment analysis (ABSA) is a fine-grained task which aims to summarize the opinions of users towards specific aspects in a sentence. ABSA normally involves three subtasks, namely aspect term extraction (AE), opinion term extraction (OE), and aspect-level sentiment classification (SC). For example, given a review "The place is small and cramped but the food is delicious," AE aims to extract a set of aspect terms {"place", "food)}. OE aims to extract a set of opinion terms {"small", "cramped", "delicious)}. Meanwhile, it is expected for SC to assign a sentiment polarity "negative" and "positive" to the aspect "place" and "food", respectively.
Figure 1: Interactive relations among subtasks in ABSA (left), and a list of abbreviations (right).
Most existing works treat ABSA as a two-step task containing AE and SC. They develop one separate method for each subtask (Tang et al., 2016; Xu et al., 2018; Li et al., 2018a; Hu et al., 2019), or take OE as an auxiliary task of AE (Wang et al., 2017; Li et al., 2018b). In order to perform ABSA for practical use, the separate methods need to be pipelined together. Recently, several studies attempt to solve ABSA in a unified framework (Wang et al., 2018a; Li et al., 2019; He et al., 2019; Luo et al., 2019).
Despite their effectiveness, we argue that these methods are not sufficient to yield satisfactory performance for the complete ABSA task. The key reason is that the interactive relations among different subtasks have been largely neglected in existing studies. These relations convey collaborative signals which can enhance the subtasks in a mutual way. For example, the opinion term "delicious" can serve as the evidence of the aspect term "food", and vice versa. In the following, we first analyze the interactive relations among different subtasks, and then present our RACL framework which is developed to exploit these relations. The detailed relations are summarized in Figure 1 (left), where each arrow  $\Leftrightarrow$  denotes one specific relation  $\mathbf{R}_{\mathrm{i}}$ .
-  $\mathbf{R}_1$  indicates the dyadic relation between AE and OE. In practice, the aspect terms must be the targets of opinion, indicating that most aspect terms like "place" can only be modified by corresponding opinion terms like "small" and "cramped" rather than a term like "delicious". Hence AE and OE might hold informative clues to each other.
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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3685-3694 July 5 - 10, 2020. ©2020 Association for Computational Linguistics
Table 1: Comparison of different methods by their capability in utilizing interactive relations.
Interactive Relations	R1	R2	R3	R4
Separate/Auxiliary	Wang et al. (2017)	✓	X	X	X
Xu et al. (2018)	X	X	X	X
Li et al. (2018b)	✓	X	X	X
Hu et al. (2019)	X	X	X	X
Unified	Wang et al. (2018a)	X	X	X	X
Li et al. (2019)	X	X	X	X
Luo et al. (2019)	X	X	X	✓
He et al. (2019)	X	X	✓	✓
Ours	✓	✓	✓	✓
-  $\mathbf{R}_2$  indicates the triadic relation between SC and  $\mathbf{R}_1$ . One critical problem in SC is to determine the dependency between the aspect and its context. For example, the context "small and cramped" plays an important role in predicting the polarity of "place". Such a dependency is highly in accordance with  $\mathbf{R}_1$  which emphasizes the interaction between the aspect and opinion terms. Hence SC and  $\mathbf{R}_1$  can help refine the selection process for each other.
-  $\mathbf{R}_{3}$  indicates the dyadic relation between SC and OE. The specific opinion terms generally convey specific polarities. For example, "fantastic" is often positive. The opinion terms extracted in OE should be paid more attention when predicting the sentiment polarity in SC.
-  $\mathbf{R}_4$  indicates the dyadic relation between SC and AE. In the complete ABSA task, the aspect terms are unknown and SC will assign a polarity to every word. The aspect terms, e.g., "place" and "food", will have their corresponding polarities, while other words are considered as the background ones without sentiment. That is to say, the results from AE should be helpful in supervising the training of SC.
When reviewing the literature on the ABSA task, we find that existing separate methods either do not utilize any relations, or only utilize  $\mathbf{R_1}$  by treating OE as an auxiliary task of AE. Meanwhile, the unified methods at most explicitly utilize  $\mathbf{R_3}$  and  $\mathbf{R_4}$ . In view of this, we propose a novel Relation-Aware Collaborative Learning (RACL) framework to fully exploit the interactive relations in the complete ABSA task. We compare our model with existing methods by their capability in utilizing interactive relations in Table 1.
RACL is a multi-layer multi-task learning framework with a relation propagation mechanism to mutually enhance the performance of subtasks. For multi-task learning, RACL adopts the shared-private scheme (Collobert and Weston, 2008; Liu et al., 2017). Subtasks AE, OE, and SC first jointly
train the low-level shared features, and then they train their high-level private features independently. In this way, the shared and private features can embed the task-invariant and task-oriented knowledge respectively. For relation propagation, RACL improves the model capacity by exchanging informative clues among three subtasks. Moreover, RACL can be stacked to multiple layers to perform collaborative learning at different semantic levels. We conduct extensive experiments on three datasets. Results demonstrate that RACL significantly outperforms the state-of-the-art methods for both the single subtasks and the complete ABSA task.
Aspect-based sentiment analysis (ABSA) is first proposed by Hu and Liu (2004) and has been widely studied in recent years (Zhang et al., 2018). We organize existing studies by how the subtasks are performed and combined to perform ABSA.
Separate Methods Most existing studies treat ABSA as a two-step task containing aspect term extraction (AE) and aspect-based sentiment classification (SC), and develop separate methods for AE (Popescu and Etzioni, 2005; Wu et al., 2009; Li et al., 2010; Qiu et al., 2011; Liu et al., 2012; Chen et al., 2014; Chernyshevich, 2014; Toh and Wang, 2014; Vicente et al., 2015; Liu et al., 2015, 2016; Yin et al., 2016; Wang et al., 2016; Li and Lam, 2017; Clercq et al., 2017; He et al., 2017; Xu et al., 2018; Yu et al., 2019), and SC (Jiang et al., 2011; Mohammad et al., 2013; Kiritchenko et al., 2014; Dong et al., 2014; Vo and Zhang, 2015; Ma et al., 2017; Wang et al., 2018b; Zhu and Qian, 2018; Chen and Qian, 2019; Zhu et al., 2019), respectively. Some of them resort to the auxiliary task opinion term extraction (OE) and exploit their relation for boosting the performance of AE. For the complete ABSA task, results from two steps must be merged together in a pipeline manner. In this way, the relation between AE/OE and SC is totally neglected, and the errors from the upstream AE/OE will be propagated to the downstream SC. The overall performance of ABSA task is not promising for pipeline methods.
Unified Methods Recently, several studies attempt to solve ABSA task in a unified framework. The unified methods fall into two types: collapsed tagging (Mitchell et al., 2013; Zhang et al., 2015; Wang et al., 2018a; Li et al., 2019) and joint training (He et al., 2019; Luo et al., 2019). The former combines the labels of AE and SC to construct
collapsed labels like  $\{B\text{-}$  senti, I-senti,  $O\}$ . The subtasks need to share all trainable features without distinction, which is likely to confuse the learning process. Moreover, the relations among subtasks cannot be explicitly modeled for this type of methods. Meanwhile, the latter constructs a multi-task learning framework where each subtask has independent labels and can have shared and private features. This allows the interactive relations among different subtasks to be modeled explicitly for the joint training methods. However, none of existing studies along this line has fully exploited the power of such relations.
We differentiate our work from aforementioned methods in that we propose a unified framework which exploits all dyadic and triadic relations among subtasks to enhance the learning capability.
Given a sentence  $S_{e} = \{w_{1},\dots ,w_{i},\dots ,w_{n}\}$ , we formulate subtasks AE, OE, and SC as three sequence labeling problems.
- AE aims to predict a tag sequence  $Y^{A} = \{y_{1}^{A},\dots,y_{i}^{A},\dots,y_{n}^{A}\}$  for aspect extraction, where  $y_{i}^{A}\in \{B,I,O\}$  denotes the beginning of, inside of, and outside of an aspect term.
- OE aims to predict a tag sequence  $Y^{O} = \{y_{1}^{O},\dots,y_{i}^{O},\dots,y_{n}^{O}\}$  for opinion extraction, where  $y_{i}^{O}\in \{B,I,O\}$  denotes the beginning of, inside of, and outside of an opinion term.
- SC aims to predict a tag sequence  $Y^{S} = \{y_{1}^{S},\dots,y_{i}^{S},\dots,y_{n}^{S}\}$  for sentiment classification, where  $y_{i}^{S}\in \{pos,neu,neg\}$  denotes the positive, neutral, and negative sentiment polarities towards each word.
Our proposed RACL is a unified multi-task learning framework which enables propagating the interactive relations (denoted as the same  $\mathbf{R}_1\ldots \mathbf{R}_4$  as those in Figure 1) for improving the ABSA performance, and it can be stacked to multiple layers to interact subtasks at different semantic levels. We present the overall architecture of RACL in Figure 2(a) and details of a single layer in Figure 2(b).
In particular, a single RACL layer contains three modules: AE, OE, and SC, where each module is designed for the corresponding subtask. These modules receive a shared representation of the input sentence, then encode their task-oriented features. After that, they propagate relations  $\mathbf{R_1}\dots \mathbf{R_4}$
for collaborative learning by exchanging informative clues to further enhance the task-oriented features. Finally, three modules will make predictions for the corresponding tag sequences  $Y^{A}$ ,  $Y^{O}$ , and  $Y^{S}$  based on the enhanced features.
In the following, we first illustrate the relation-aware collaborative learning in one layer, then show the stacking and the training of the entire RACL.
3.3 Relation-Aware Collaborative Learning
Input Word Vectors Given a sentence  $S_{e}$ , we can map the word sequence in  $S_{e}$  with either pretrained word embeddings (e.g., GloVe) or pretrained language encoders (e.g., BERT) to generate a sequence of word vectors  $\mathbf{E} = \{e_1, \dots, e_i, \dots, e_n\} \in \mathbb{R}^{d_w \times n}$ , where  $d_{w}$  is the dimension of word vectors. We will examine the effects of these two types of word vectors in the experiments.
Multi-task Learning with Shared-Private Scheme To perform multi-task learning, different subtasks should focus on the different characteristics of a shared training sample. Inspired by the shared-private scheme (Collobert and Weston, 2008; Liu et al., 2017), we extract both the shared and private features to embed task-invariant and task-oriented knowledge for the AE, OE, and SC modules.
To encode the shared task-invariant features, we simply feed each  $e_i$  in  $\mathbf{E}$  into a fully-connected layer and generate a transformed vector  $h_i \in \mathbb{R}^{d_h}$ . We then obtain a sequence of shared vectors  $\mathbf{H} = \{h_1, \dots, h_i, \dots, h_n\} \in \mathbb{R}^{d_h \times n}$  for each sentence which will be jointly trained by all subtasks.
Upon the shared task-invariant features  $\mathbf{H}$ , the AE, OE, and SC modules will encode the task-oriented private features for the corresponding subtasks. We choose a simple CNN as the encoder function  $F$  due to its high computation efficiency.
For subtasks AE and OE, the key features for determining the existence of aspect and opinion terms are the representations of the original and adjacent words. Therefore, we construct two encoders to extract local AE-oriented features  $\mathbf{X}^A$  and  $OE$ -oriented features  $\mathbf{X}^O$ :
$$
\begin{array}{l} \bar {F} ^ {A}: \mathbf {H} \rightarrow \mathbf {X} ^ {A}, \mathbf {X} ^ {A} \in \mathbb {R} ^ {d _ {c} \times n}, \\ F ^ {O}: \mathbf {H} \rightarrow \mathbf {X} ^ {O}, \mathbf {X} ^ {O} \in \mathbb {R} ^ {d _ {c} \times n} \\ \end{array}
$$
For subtask SC, the process of feature generation is different from that in AE/OE. In order to determine the sentiment polarity towards an aspect term, we need to extract related semantic information from its context. The critical problem in SC is to determine the dependency between an aspect
(a) Architecture with  $L$  stacked RACL layers.
(b) Details of a single RACL layer.
Figure 2: The proposed RACL framework.
term and its context. Moreover, in the complete ABSA task, the aspect terms are unknown in SC and it needs to assign a polarity to every word in  $S_{e}$ . Based on these observations, we first encode the contextual features  $\mathbf{X}^{ctx}$  from  $\mathbf{H}$ :
$$
F ^ {c t x}: \mathbf {H} \rightarrow \mathbf {X} ^ {c t x}, \mathbf {X} ^ {c t x} \in \mathbb {R} ^ {d _ {h} \times n} \tag {2}
$$
Then we treat the shared vector  $\pmb{h}_i$  as the query aspect and compute the semantic relation between the query and contextual features using the attention mechanism:
$$
\begin{array}{l} d s _ {i, j} ^ {(i \neq j)} = \left(\left(\boldsymbol {h} _ {\boldsymbol {i}}\right) ^ {T} \times \mathbf {X} _ {j} ^ {c t x}\right) \cdot \left[ l o g _ {2} (2 + | i - j |) \right] ^ {- 1}, \\ \mathbf {M} _ {i, j} ^ {c t x} = \frac {\exp \left(d s _ {i , j}\right)}{\sum_ {k = 1} ^ {n} \exp \left(d s _ {i , k}\right)}, \tag {3} \\ \end{array}
$$
where  $ds_{i,j}^{(i\neq j)}$  denotes the dependency strength between the  $i$ -th query word and the  $j$ -th context word, and  $\mathbf{M}_{i,j}^{ctx}$  is the normalized attention weight of  $ds_{i,j}^{(i\neq j)}$ . We add a coefficient  $[log_2(2 + |i - j|)]^{-1}$  based on the absolute distance between two words. The rationale is that the adjacent context words should contribute more to the sentiment polarity. Finally, for the aspect query  $w_{i}$ , we can obtain the global SC-oriented features  $\mathbf{X}_i^S$  by a weighted sum of all contextual features (except the one for  $w_{i}$ ):
$$
\mathbf {X} _ {i} ^ {S} = \sum_ {j = 1} ^ {n} \left(\mathbf {M} _ {i, j} ^ {c t x} \cdot \mathbf {X} _ {j} ^ {c t x}\right) \tag {4}
$$
Propagating Relations for Collaborative Learning After encoding task-oriented features, we propagate the interactive relations  $(\mathbf{R}_1.. \mathbf{R}_4)$  among subtasks to mutually enhance the AE, OE, and SC modules.
(1)  $\mathbf{R}_1$  is the dyadic relation between AE and OE, which indicates that AE and OE might hold informative clues to each other. In order to model  $\mathbf{R}_1$ , we want the AE-oriented features  $\mathbf{X}^A$  and the OE-oriented features  $\mathbf{X}^O$  to exchange useful information based on their semantic relations. Take the subtask AE as an example, the semantic relation
between the word in AE and that in OE is defined as follows:
$$
\begin{array}{l} s r _ {i, j} ^ {(i \neq j)} = \left(\mathbf {X} _ {i} ^ {A}\right) ^ {T} \times \mathbf {X} _ {j} ^ {O}, \\ \mathbf {M} _ {i, j} ^ {O 2 A} = \frac {\exp \left(s r _ {i , j}\right)}{\sum_ {k = 1} ^ {n} e x p \left(s r _ {i , k}\right)} \tag {5} \\ \end{array}
$$
For the word  $w_{i}$  in AE, we can obtain the useful clues  $\mathbf{X}_i^{O2A}$  from OE by applying a weighted sum of semantic relations to all words in OE (except the word  $w_{i}$  itself), i.e.,
$$
\mathbf {X} _ {i} ^ {O 2 A} = \sum_ {j = 1} ^ {n} \left(\mathbf {M} _ {i, j} ^ {O 2 A} \cdot \mathbf {X} _ {j} ^ {O}\right) \tag {6}
$$
We then concatenate the original AE-oriented features  $\mathbf{X}^A$  and the useful clues  $\mathbf{X}^{O2A}$  from OE as the final features for AE, and feed them into a fully-connected layer to predict the tags of aspect terms:
$$
Y ^ {A} = \operatorname {s o f t m a x} \left(\mathbf {W} ^ {A} \left(\mathbf {X} ^ {A} \oplus \mathbf {X} ^ {O 2 A}\right)\right), \tag {7}
$$
where  $\mathbf{W}^A\in \mathbb{R}^{3\times 2d_c}$  is a transformation matrix,  $\mathbf{Y}^A\in \mathbb{R}^{3\times n}$  is the predicted tag sequence of AE.
For subtask OE, we use the transposed matrix of  $sr_{i,j}^{(i \neq j)}$  in Eq. 5 to compute the corresponding  $\mathbf{M}^{A2O}$ . In this way, the semantic relation between AE and OE will be consistent without regard to the direction. Then we can obtain the useful clues  $\mathbf{X}^{A2O}$  from AE and generate the predicted tag sequence  $Y^O \in \mathbb{R}^{3 \times n}$  in a similar way, i.e.,
$$
Y ^ {O} = \operatorname {s o f t m a x} \left(\mathbf {W} ^ {O} \left(\mathbf {X} ^ {O} \oplus \mathbf {X} ^ {A 2 O}\right)\right) \tag {8}
$$
Additionally, each  $w_{i}$  cannot be an aspect term and an opinion term at the same time, so we add a regularization hinge loss to constrain  $Y^{A}$  and  $Y^{O}$ :
$\mathcal{L}^R = \sum_{i=1}^n \max(0, P_{y_i^A \in \{B, I\}} + P_{y_i^O \in \{B, I\}} - 1.0)$ , (9) where  $P$  denotes the probability under the given conditions.
(2)  $\mathbf{R}_2$  is the triadic relation between SC and  $\mathbf{R}_1$ . Remember that the dependency between the aspect term and its context is critical for subtask SC, and
we have already calculated this dependency using the normalized attention weight  $\mathbf{M}^{ctx}$ . Hence we can model  $\mathbf{R}_2$  by propagating  $\mathbf{R}_1$  to  $\mathbf{M}^{ctx}$ . We use  $\mathbf{M}^{O2A}$  as the representative of  $\mathbf{R}_1$ , and add it on  $\mathbf{M}^{ctx}$  to denote the influence from  $\mathbf{R}_1$  to SC. More formally, we define  $\mathbf{R}_2$  as the following operation:
$$
\mathbf {M} _ {i, j} ^ {c t x} \leftarrow \mathbf {M} _ {i, j} ^ {c t x} + \mathbf {M} _ {i, j} ^ {O 2 A} \tag {10}
$$
Actually,  $\mathbf{M}^{O2A}$  characterizes the dependency between aspect terms and contexts in the view of term extraction while  $\mathbf{M}^{ctx}$  characterizes it in the view of sentiment classification. The dual-view relation  $\mathbf{R}_2$  can help refine the selection processes for both extraction and classification subtasks.
(3)  $\mathbf{R}_3$  is the dyadic relation between SC and OE, which indicates that the extracted opinion terms should be paid more attention when predicting the sentiment polarity. In order to model  $\mathbf{R}_3$ , similarly to the method for  $\mathbf{R}_2$ , we update  $\mathbf{M}^{ctx}$  in SC using the generated tag sequence  $Y^O$  from OE:
$$
\mathbf {M} _ {i, j} ^ {c t x} \leftarrow \mathbf {M} _ {i, j} ^ {c t x} + P _ {y _ {j} ^ {O} \in \{B, I \}} \cdot \left[ \log_ {2} (2 + | i - j |) \right] ^ {- 1} \tag {11}
$$
By doing this, the opinion terms can get larger weights in the attention mechanism. Consequently, they will contribute more to the prediction of the sentiment polarity.
After getting the interacted values for  $\mathbf{M}^{ctx}$ , we can recompute the SC-oriented features  $\mathbf{X}^S$  in Eq.4 accordingly. Then we concatenate  $\mathbf{H}$  and  $\mathbf{X}^S$  as the final features for SC and feed them into a fully-connected layer to predict sentiment polarities for the candidate aspect terms:
$$
Y ^ {S} = \operatorname {s o f t m a x} \left(\mathbf {W} ^ {S} \left(\mathbf {H} \oplus \mathbf {X} ^ {S}\right)\right), \tag {12}
$$
where  $\mathbf{W}^S\in \mathbb{R}^{3\times 2d_h}$  is a transformation matrix,  $Y^{S}\in \mathbb{R}^{3\times n}$  is the predicted tag sequence of SC.
(4)  $\mathbf{R}_4$  is the dyadic relation between SC and AE, which indicates that the results from AE are helpful in supervising the training of SC. Clearly, only aspect terms have sentiment polarities. Although SC needs to assign a polarity to every word, we know the ground truth aspect terms in AE during the training process. Therefore, we directly use the ground truth tag sequence  $\hat{Y}^A$  of AE to refine the labeling process in SC. Specifically, only the predicted tags towards true aspect terms would be counted in the training procedure:
$$
y _ {i} ^ {S} \leftarrow \mathbf {I} \left(\hat {y} _ {i} ^ {A}\right) \cdot y _ {i} ^ {S}, \tag {13}
$$
where  $\mathbf{I}(\hat{y}_i^A)$  equals to 1 if  $w_i$  is an aspect term and to 0 if not. Notice that this approach is only used in the training procedure.
3.4 Stacking RACL to Multiple Layers
When using one single RACL layer, AE, OE, and SC modules only extract corresponding features in a relatively low linguistic level, which may be insufficient to serve as the evidence to label each word. Hence we stack RACL to multiple layers to obtain high-level semantic features for subtasks, which helps to conduct deep collaborative learning.
Specifically, we first encode features  $\mathbf{X}^{ctx(1)}$ ,  $\mathbf{X}^{A(1)} \oplus \mathbf{X}^{O2A(1)}$ , and  $\mathbf{X}^{O(1)} \oplus \mathbf{X}^{A2O(1)}$  in layer(1). Then in layer(2), we input these features for SC, AE, and OE to generate  $\mathbf{X}^{ctx(2)}$ ,  $\mathbf{X}^{A(2)}$ , and  $\mathbf{X}^{O(2)}$ . In this way, we can stack RACL to  $L$  layers. We then conduct average pooling on results from all layers to obtain the final prediction:
$$
Y ^ {T} = a v g \left(\left[ Y ^ {T (1)}, Y ^ {T (2)}, \dots , Y ^ {T (L)} \right]\right), \tag {14}
$$
where  $T \in \{A, O, S\}$  denotes the specific subtask, and  $L$  is the number of layers. This shortcut-like architecture can enforce the features in the low layers to be meaningful and informative, which in turn helps the high layers to make better predictions.
After generating the tag sequences  $Y^{A}, Y^{O}$ , and  $Y^{S}$  for the sentence  $S_{e}$ , we compute the cross-entropy loss of each subtask:
$$
\mathcal {L} ^ {T} = - \sum_ {i = 1} ^ {n} \sum_ {j = 1} ^ {J} \hat {y _ {i j} ^ {T}} \cdot \log \left(y _ {i j} ^ {T}\right), \tag {15}
$$
where  $T \in \{A, O, S\}$  denotes the subtask,  $n$  is the length of  $S_e$ ,  $J$  is the category of labels,  $y_i^T$  and  $y_i^{T}$  are the predicted tags and ground truth labels.
The final loss  $\mathcal{L}$  of RACL is the combination of the loss for subtasks and the loss for regularization, i.e.,  $\mathcal{L} = \sum \mathcal{L}^T +\lambda \cdot \mathcal{L}^R$  , where  $\lambda$  is a coefficient. We then train all parameters with back propagation.
Datasets We evaluate RACL on three real-world ABSA datasets from SemEval 2014 (Pontiki et al., 2014) and 2015 (Pontiki et al., 2015), which include reviews from two domains: restaurant and laptop. Original datasets only have ground truth labels for aspect terms and corresponding sentiment polarities, while labels for opinion terms are annotated by two previous works (Wang et al., 2016, 2017). All datasets have a fixed training/test split. We further randomly sample  $20\%$  training data as the development set to tune hyper-parameters, and only use the remaining  $80\%$  for training. The statistics for datasets are summarized in Table 2.
Table 2: The statistics of datasets.
Datasets	Type	Sentence	Aspect	Opinion
Restaurant14	train	3044	3699	3484
test	800	1134	1008
Laptop14	train	3048	2373	2504
test	800	654	674
Restaurant15	train	1315	1199	1210
test	685	542	510