SlowGuess/NAACL_2021_OCR huggingface dataset:
Yuhao Feng $^{1}$ , Yanghui Rao $^{1,*}$ , Yuyao Tang $^{2}$ , Ninghua Wang $^{2}$ , He Liu $^{2}$
$^{1}$ School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China  $^{2}$ Tencent Inc, Shenzhen, China
fengyh3@mail2.sysu.edu.cn; raoyangh@mail.sysu.edu.cn {youngtang,ninghuawang,heziliu}@tencent.com
Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be further applied to Aspect-Opinion Pair Extraction (AOPE) which aims at extracting aspects (i.e., opinion targets) and opinion terms in pairs. In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. As a case study, we also develop a Multi-Task structure named MT-TSMSA for AOPE by combining our TSMSA with an aspect and opinion term extraction module. Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly; meanwhile, the performance of MT-TSMSA is similar or even better than state-of-the-art AOPE baseline models.
Aspect-Based Sentiment Analysis (ABSA) (Pontiki et al., 2014) has attracted much attention of researchers in recent years. In ABSA, aspect (or called opinion target) extraction and opinion term extraction are two fundamental tasks. Aspect is the word or phrase in the reviews referring to the object towards which users show attitudes, while opinion terms are those words or phrases representing users' attitudes (Wu et al., 2020). For example, in the sentence "The dim sum is delicious," the phrase "dim sum" is an aspect and the word "delicious" is an opinion term. See the upper part of Table 1 for more examples. Plenty of works based on neural networks have been done in both aspect
Reviews:
"Soooo great! The food is delicious and inexpensive, and the environment is in a nice. The only problem is that the soup and dessert are ordinary."
Aspect-Oinion Pairs:
food : [delicious, inexpensive] (one-to-many)  
environment : [nice] (one-to-one)  
soup, dessert : [ordinary] (many-to-one)
Table 1: The upper part is a restaurant review and the lower part shows the corresponding aspect-opinion pairs. Extracted aspects and opinion terms are marked in red and blue, respectively.
and opinion term extraction (Liu et al., 2015; Poria et al., 2016; Xu et al., 2018); moreover, some studies combine these two tasks into a multi-task structure to extract aspects and opinion terms simultaneously (Wang et al., 2016, 2017; Li and Lam, 2017; Dai and Song, 2019).
However, one critical deficiency in the researches mentioned above is that they ignore the relation of aspects and opinion terms, which leads to the birth of Target-oriented Opinion Words (or Terms) Extraction (TOWE) (Fan et al., 2019) for extracting the corresponding opinion terms of a given opinion target. Subsequently, Aspect-Opinion Pair Extraction (AOPE) (Chen et al., 2020) and Pair-wise Aspect and Opinion Terms Extraction (PAOTE) (Zhao et al., 2020) have emerged, which both aim at extracting aspects and opinion terms in pairs. AOPE and PAOTE are exactly the same task, only named differently. In the following, we use AOPE to denote this task for simplicity. It can be considered that AOPE contains aspect and opinion word extraction and TOWE. Since aspect extraction has been fully studied and satisfactory results have been obtained, TOWE, which aims at mining the relation between aspects and opinion terms, is the key to the AOPE task. As shown in the lower part of Table 1, the relational structure of
*The corresponding author.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1805-1815 June 6-11, 2021. ©2021 Association for Computational Linguistics
the aspect-opinion pairs within a sentence can be complicated, including one-to-one, one-to-many, and many-to-one.
The challenge of TOWE is the learning of representations of the given opinion target accurately and a few works focus on this task. For instance, Fan et al. (2019) propose an Inward-Outward LSTM to pass target information to the left context and the right context of the target respectively, and then they combine the left, right, and global context to encode the sentence. Recently, SDRN (Chen et al., 2020) and SpanMlt (Zhao et al., 2020) both adopt a pre-trained language model to learn contextual representations for AOPE. In SDRN, a double-channel recurrent network and a synchronization unit are applied to extract aspects, opinion terms and their relevancy. In SpanMlt, the terms are extracted under annotated span boundaries with contextual representations, and then the relations between every two span combinations are identified. However, apart from hyper-parameters in the pre-trained language model, these two methods introduce many other hyper-parameters (e.g., the hidden size, thresholds and recurrent steps in SDRN, and the span length, top  $k$  spans and the balanced factor of different tasks in SpanMlt). Some of these hyper-parameters have a significant impact on the model performance.
Motivated by the previous work and to address the challenges mentioned above, we propose a Target-Specified sequence labeling method based on Multi-head Self-Attention (Vaswani et al., 2017) (TSMSA). The sentence is first processed in the format "[SEP] Aspect [SEP]" (e.g., "The [SEP] food [SEP] is delicious."), which is inspired by Soares et al. (2019) who utilized a special symbol "[SEP]" to label all entities and output their corresponding representations. Then we develop a sequence labeling model based on multi-head self-attention to identify the corresponding opinion terms. By using the special symbol and self-attention mechanism, TSMSA is capable of capturing the information of the specific aspect. To improve the performance of our model, we apply pre-trained language models like BERT (Devlin et al., 2019) which contain a multi-head self-attention module as the encoder. As a case study, we integrate aspect and opinion term extraction, and TOWE into a Multi-Task architecture named MT-TSMSA to validate the effectiveness of our method on the AOPE task. In addition, apart from hyper-parameters in the pre
trained language model, we only need to adjust the balanced factor of different tasks in MT-TSMSA. In summary, our main contributions are as follows:
- We propose a target-specified sequence labeling method with multi-head self-attention mechanism to perform TOWE, which generates target-specific context representations for different targets in the same review with the special symbol and multi-head self-attention. Pre-trained language models can be conveniently applied to improve the performance.
- For our TSMSA and MT-TSMSA, only a small amount of hyper-parameters need to be adjusted when using pre-trained language models. Compared to the existing models for TOWE and AOPE, we alleviate the tradeoff issue between a model's complexity and performance.
Extensive experiments validate that our TSMSA can achieve the best performance on TOWE, and MT-TSMSA performs quite competitive on AOPE. The rest of this paper is organized as follows. Section 2 introduces the existing studies on TOWE and AOPE, respectively. Section 3 details the proposed TSMSA and MT-TSMSA. Section 4 presents our experimental results and discussions. Finally, we draw conclusions in Section 5.
2.1 Target-oriented Opinion Words Extraction
Plenty of works have been carried out for aspect extraction and opinion term extraction. Early researches can be divided into unsupervised/semi-supervised methods (Hu and Liu, 2004; Zhuang et al., 2006; Qiu et al., 2011) and supervised methods (Jakob and Gurevych, 2010; Shu et al., 2017). With the development of neural networks, deep learning methods (Liu et al., 2015; Yin et al., 2016; Poria et al., 2016; Xu et al., 2018) have made impressive progress in recent years. Several works integrate aspect extraction and opinion term extraction into a co-extraction process. Qiu et al. (2011) expand the list of aspects and opinion terms in a bootstrapping method by double propagation. Some other works adopt the co-extraction structure in neural networks with multi-task learning (Wang et al., 2016, 2017; Li and Lam, 2017).
However, the above methods ignore the relation between aspects and opinion terms and only a few
works focus on this field. Rule-based methods (Hu and Liu, 2004; Zhuang et al., 2006) are proposed to select corresponding opinion terms with distance rule and syntactic rule templates based on dependency parsing trees. However, the performance of these methods heavily relies on expert knowledge and these rules usually cover only a small amount of cases. Fan et al. (2019) carry out TOWE by extracting the corresponding opinion terms for a given aspect, and then utilize Inward-Outward LSTM to generate implicit representations of aspects. Nevertheless, this approach is not capable of applying powerful pre-trained language models like BERT as the encoder to perform better. Our model aims to extract corresponding opinion terms of the given aspect with explicit representations, in addition to boost performance by employing BERT as the encoder.
2.2 Aspect-Oinion Pair Extraction
Aspect-Oinion Pair Extraction (AOPE) (Chen et al., 2020) and Pair-wise Aspect and Opinion Terms Extraction (PAOTE) (Zhao et al., 2020) both aim at extracting aspects and opinion terms in pairs. AOPE and PAOTE are essentially the same task with different names, and they can be split into aspect extraction and TOWE. Chen et al. (2020) propose a Synchronous Double-channel Recurrent Network (SDRN) which consists of an opinion entity extraction unit, a relation detection unit, and a synchronization unit for pair extraction. Zhao et al. (2020) develop a span-based multi-task learning framework (SpanMlt) where the terms are extracted under annotated span boundaries, so as to identify the relations between every two span combinations.
However, SDRN contains a lot of hyperparameters and SpanMlt generates a great many of candidate spans if the value of maximal length of a span is large or the sentence is too long. The advantage of our methods is that only a small amount of hyper-parameters adjustment is required and similar or even better performance can be achieved.
Given a sentence  $s = \{w_1, w_2, \dots, w_n\}$  consisting of  $n$  words, an aspect (opinion target)  $a = \{w_i, w_{i+1}, \dots, w_{i+k}\}$ , and an opinion term  $o = \{w_j, w_{j+1}, \dots, w_{j+m}\}$  ( $a$  and  $o$  are substrings of  $s$ ), the probabilities of target-oriented opinion terms are defined as  $p(o|s,a)$  in the TOWE task and the
probabilities of aspect-opinion pairs are defined as  $p(\langle a,o\rangle |s) = p(a|s)\times p(o|s,a)$  in the AOPE task. The BIO tagging scheme (Ramshaw and Marcus, 1995) and a special symbol "[SEP]" are applied to this task, where each word  $w_{i}$  in the sentence  $s$  is tagged as  $y_{i}\in \{\mathrm{B},\mathrm{I},\mathrm{O},[\mathrm{SEP}]\}$  (B: Beginning, I: Inside, O: Others, [SEP]: the tag of an aspect).
The structures of our Target-Specified sequence labeling method based on Multi-head Self-Attention (TSMSA) and the Multi-Task version (MT-TSMSA) are shown in Figure 1 (c) and (d). As aforementioned, we first use a special symbol "[SEP]" to label each aspect. Next, the multi-head self-attention method is applied to capture the context representations of the specific aspect explicitly, then they are passed to a projection layer and a Conditional Random Field (CRF) (Lafferty et al., 2001) layer for sequence labeling. Furthermore, the aspect and opinion words extraction (task 0) as well as the target-oriented opinion words extraction (task 1) are combined for multi-task learning. These two tasks share the parameters of encoder but differ in projection and CRF layers.
3.3 Multi-Head Self-Attention
We describe the multi-head self-attention approach according to Vaswani et al. (2017) with the details shown in Figure 1 (a) and (b). For each attention head in the above approach, we first compute the scaled dot-product attention. Particularly, the input consists of a set of queries, keys, and values, where  $d_{k}$  stands for the dimension of queries and keys, and  $d_{v}$  represents the dimension of values. Then they are packed together into matrices  $Q$ ,  $K$ , and  $V$ , respectively. The scaled dot-product attention is calculated as follows:
$$
\operatorname {A t t e n t i o n} (Q, K, V) = \operatorname {s o f t m a x} \left(\frac {Q K ^ {\mathrm {T}}}{\sqrt {d _ {k}}}\right) V. \tag {1}
$$
Next, given the number of attention heads  $h$ , we can get the dimension of output  $d_{model} = h \times d_v$ . Finally, the multi-head attention is described as follows:
$$
M H (I, h) = \operatorname {C o n c a t} \left(\text {h e a d} _ {1}, \dots , \text {h e a d} _ {h}\right) W ^ {O}, \tag {2}
$$
$$
h e a d _ {i} = A t t e n t i o n \left(I W _ {i} ^ {Q}, I W _ {i} ^ {K}, I W _ {i} ^ {V}\right), \quad (3)
$$
where  $I = \{\vec{i_1},\vec{i_2},\dots,\vec{i_n}\}$  (the dimension of  $\vec{i}$  is  $d_{model}$ ) indicates the input and  $n$  is the sequence
(a) Scaled Dot-Product Attention
(b) Multi-Head Attention
(c) TSMSA  $\mathrm{(base)}$
(d) MT-TSMSA  $\mathrm{(base)}$
Figure 1: The structures of our TSMSA and MT-TSMSA base models are presented in (c) and (d). For clarity, the details about Multi-head Attention are shown in (a) and (b).
length. The parameter matrices of projections are  $W_{i}^{Q} \in \mathbb{R}^{d_{model} \times d_{k}}$ ,  $W_{i}^{K} \in \mathbb{R}^{d_{model} \times d_{k}}$ ,  $W_{i}^{V} \in \mathbb{R}^{d_{model} \times d_{v}}$ , and  $W_{i}^{O} \in \mathbb{R}^{d_{model} \times d_{model}}$ .
3.4 Target-Specified Encoder
To start with, the input vector of each word is generated by utilizing a word embedding lookup table  $\mathbb{L}_w\in \mathbb{R}^{r\times d_w}$  and a positional embedding lookup table  $\mathbb{L}_p\in \mathbb{R}^{n\times d_p}$ , where  $d_{w}$  is the dimension of word embeddings,  $r$  is the vocabulary size, and  $d_{p}$  is the dimension of positional embeddings. These embedding lookup tables will map  $s = \{w_1,\dots,w_n\}$  to  $\{\vec{e}_1^w,\dots,\vec{e}_n^w\}$  and  $\{\vec{e}_1^p,\dots,\vec{e}_n^p\}$ , respectively. For our base models (not using a pre-trained language model),  $\vec{e}_i^w$  will be projected to a low dimensional vector  $\vec{e}_i^{low}$  which is calculated as follows:  $\vec{e}_i^{low} = \sigma (W^e\vec{e}_i^w)$ , where  $W^{e}\in \mathbb{R}^{d_{low}\times d_{w}}$  ( $d_{low} < d_w$ ) denotes the matrix of projection and  $\sigma (\cdot)$  is the activation function. In this case,  $\vec{t_i}$  in the input  $T = \{\vec{t_1},\dots,\vec{t_n}\}$  is represented by  $[\vec{e}_i^{low};\vec{e}_i^{p}]$  and  $d_{model} = d_{low} + d_p$ . For a pre-trained language model like BERT (Devlin et al., 2019),  $\vec{t_i}$  equals the sum of  $\vec{e}_i^w$ ,  $\vec{e}_i^p$ , and  $\vec{e}_i^s$ , where  $e^s = \{\vec{e}_1^s,\dots,\vec{e}_n^s\}$  (the dimension of  $\vec{e}_i^s$  is  $d_p$ ) represents segment embeddings, and  $d_{model} = d_p = d_w$ .
Then, the input vector  $T$  is passed to multihead self-attention modules, where a feed-forward network and an add-norm network are combined in sequence to generate the context representation of each layer  $H = \{H^{1},\dots,H^{l}\}$ , where  $l$  is the number of multi-head attention layers and  $H^{i} = \{\vec{H}_{1}^{i},\dots,\vec{H}_{n}^{i}\}$ .  $H^{i}$  can be calculated as
$$
O ^ {i} = M H \left(H ^ {i - 1}, h\right), \tag {4}
$$
$$
F F N ^ {i} = \max  \left(0, O ^ {i} W _ {1} ^ {i} + b _ {1} ^ {i}\right) W _ {2} ^ {i} + b _ {2} ^ {i}, \quad (5)
$$
$$
H ^ {i} = L N \left(H ^ {i - 1} + F F N ^ {i}\right), \tag {6}
$$
where  $h$  is the number of attention heads,  $H^0 = T$ , the matrices  $W_1^i \in \mathbb{R}^{d_{model} \times d_{ff}}$  and  $W_2^i \in \mathbb{R}^{d_{ff} \times d_{model}}$  represent mappings from  $d_{model}$  to  $d_{ff}$  and back to  $d_{model}$ .  $LN(\cdot)$  is a layer normalization method applying to sequential data (Ba et al., 2016). Finally, the output of the encoder is  $H^l$ , i.e., the last layer of  $H$ .
3.5 Decoder and Training
Given a sequential representation  $H^l$  and a sequential label  $Y = \{y_1, \dots, y_n\}$  ( $y_i \in \{\mathrm{B}, \mathrm{I}, \mathrm{O}, [\mathrm{SEP}]\}$  or  $y_i \in \{\mathrm{B - ASP}, \mathrm{I - ASP}, \mathrm{B - OP}, \mathrm{I - OP}, \mathrm{O}\}^1$ ), we can use  $H^l$  to compute  $p(Y|H^l)$ . Greedy decoding or CRF can be adopted in the decoding process. CRF is chosen as our decoding strategy because CRF has the ability to capture the correlations between tokens and labels and the correlations between adjacent labels simultaneously. Given a new sentence, we use Viterbi algorithm (Viterbi, 1967) to predict the label sequence by maximizing the conditional probability  $p(Y|H^l)$  in the decoding process.
3.5.1 Single-Task Version
The single-task version of our approaches is TSMSA. Given a predicted label sequence  $Y$  and
$^{1}$ B-ASP: beginning of an aspect, I-ASP: inside of an aspect, B-OP: beginning of an opinion term, I-OP: inside of an opinion term, and O: others.
a sequential representation  $H^l$ , the score function  $S(H^l, Y)$  can be defined as follows:
$$
\begin{array}{l} S \left(H ^ {l}, Y\right) = \sum_ {i = 1} ^ {n} Q _ {y _ {i - 1}, y _ {i}} + \sum_ {i = 1} ^ {n} P _ {i, y _ {i}}, (7) \\ P = H ^ {l} W _ {p} + b _ {p}, (8) \\ \end{array}
$$
where the matrix  $Q \in \mathbb{R}^{k \times k}$  captures the relation of adjacent labels, the matrix  $P \in \mathbb{R}^{n \times k}$  learns the relation of tokens and labels, and the matrices  $W_{p} \in \mathbb{R}^{d_{model} \times k}$  and  $b_{p} \in \mathbb{R}^{n \times k}$  indicate a projection operation from dimension  $d_{model}$  to dimension  $k$ . In the above,  $k$  means the dimension of the label space. Then, the linear-chain CRF is exploited to calculate the conditional probability of the predicted sequence  $Y$  as follows:
$$
p (Y | H ^ {l}) = \frac {\exp (S (H ^ {l} , Y))}{\sum_ {\tilde {Y} \in Y _ {a l l}} \exp (S (H ^ {l} , \tilde {Y}))}, \tag {9}
$$
where  $Y_{all}$  denotes the set of all possible sequential labels. So the loss of a sentence can be calculated by the negative log likelihood as follows:
$$
L (s) = - \log p (Y | H ^ {l}). \tag {10}
$$
3.5.2 Multi-Task Version
By integrating aspect and opinion term extraction (task 0) and TOWE (task 1) into a multi-task architecture, we propose a MT-TSMSA method for AOPE. MT-TSMSA can be defined as using a sentence  $H^{l}$  and a task id  $\in \{0,1\}$  to calculate the conditional probability  $p(Y|H^l, id)$ . When the task id equals 0, it means aspect and opinion term extraction. For TOWE, the task id is 1. Some examples are shown in Figure 1 (d). Aiming at handling different tasks, different score functions  $S_{0}(H^{l}, Y^{0})$  and  $S_{1}(H^{l}, Y^{1})$  are defined, where  $S_{0}(\cdot)$  and  $S_{1}(\cdot)$  have different parameter matrices,  $Y^{0}$  ( $Y_{i}^{0} \in \{\text{B-ASP, I-ASP, B-OP, I-OP O}\}$ ) and  $Y^{1}$  ( $Y_{i}^{1} \in \{\text{B, I, O, [SEP]}\}$ ) represent the sequential labels of aspect and opinion term extraction, and TOWE, respectively. So the conditional probabilities of the predicted sequences  $Y^{0}$  and  $Y^{1}$  can be calculated as follows:
$$
\begin{array}{l} p \left(Y ^ {0} \mid H ^ {l}, i d = 0\right) = \frac {\exp \left(S _ {0} \left(H ^ {l} , Y ^ {0}\right)\right)}{\sum_ {\tilde {Y} \in Y _ {a l l} ^ {0}} \exp \left(S _ {0} \left(H ^ {l} , \tilde {Y}\right)\right)}, (11) \\ p \left(Y ^ {1} \mid H ^ {l}, i d = 1\right) = \frac {\exp \left(S _ {1} \left(H ^ {l} , Y ^ {1}\right)\right)}{\sum_ {\tilde {Y} \in Y _ {a l l} ^ {1}} \exp \left(S _ {1} \left(H ^ {l} , \tilde {Y}\right)\right)}, (12) \\ \end{array}
$$
where  $Y_{all}^{0}$  denotes the set of all possible sequential labels of task 0 and  $Y_{all}^{1}$  represents the set of all possible sequential labels of task 1. The loss of a sentence is also calculated by the negative log likelihood as follows:
$$
L (s, i d) = - \log p (Y | H ^ {l}, i d). \tag {13}
$$
Given  $M$  sentences  $S = \{s_1,s_2,\dots ,s_M\}$  with  $id = \{id_{1},\ldots ,id_{M}\}$ , we can minimize the loss for training:
$$
J (\theta) = \sum_ {k = 1} ^ {M} ((1 - i d _ {k}) \lambda + i d _ {k}) L \left(s _ {k}, i d _ {k}\right), \tag {14}
$$
where  $\lambda$  is the hyper-parameter used to balance these two tasks.
3.6 Inference Process
For TOWE, a sentence with a given aspect (i.e., target) is first processed into target-specified mode ("[SEP] Aspect [SEP]") with the special symbol "[SEP]" and then passed into TSMSA, the outputs of which are the target-oriented opinion terms. For AOPE, MT-TSMSA generates aspect-opinion pairs by a two-stage inference process. Firstly, a sentence is passed into MT-TSMSA, where aspects are extracted in task 0. Secondly, given extracted aspects, repeating the inference process of TOWE, MT-TSMSA outputs the target-oriented opinion terms from task 1. Accordingly, the combinations of aspects from task 0 and target-orient opinion terms from task 1 are aspect-opinion pairs.
To evaluate the performance of our model $^2$ , we conduct experiments on two public datasets from laptop and restaurant domains. These two datasets were respectively built by Fan et al. (2019) for TOWE and Chen et al. (2020) for AOPE based on SemEval Challenge 2014 Task 4, SemEval Challenge 2015 Task 12, and SemEval Challenge 2016 Task 5 (Pontiki et al., 2014, 2015, 2016). For the first dataset, every sentence was annotated by two people, and the conflicts were checked and eliminated manually. The second dataset was developed by extending the first one. The statistics of these benchmark datasets are shown in Table 2, from
The code of our model is available in public at: https://github.com/fengyh3/TSMSA.
which we can observe that the second dataset includes many negative samples for AOPE (i.e., the sentences only contain aspects and opinion terms, without any aspect-opinion pairs). Note that these negative samples will also be considered when testing our model on AOPE.
Provider	Datasets	#Sent	#Target	#A	#None
(Fan et al., 2019)	14lap	train	1158	1634	1626	0
test	343	482	481	0
14res	train	1627	2643	2638	0
test	500	865	864	0
15res	train	754	1076	1076	0
test	325	436	436	0
16res	train	1079	1512	1512	0
test	329	457	456	0
(Chen et al., 2020)	14lap	train	3045	1535	2359	1297
test	800	380	653	334
14res	train	3041	2809	3693	902
test	800	936	1134	219
15res	train	1315	1231	1205	556
test	685	516	542	352
Table 2: Statistics of datasets. #Sent, #Target, #A, and #None represent the numbers of sentences, relations, and aspects, and the sentences without any aspects and opinion terms, respectively.
Fan et al. (2019) have employed various baselines in TOWE, including Distance-rule (Hu and Liu, 2004), Dependency-rule (Zhuang et al., 2006), BiLSTM + Distance-rule, and TC-BiLSTM, except for BERT-based methods. To achieve comprehensive comparative analysis, we develop baselines of BERT + Distance-rule and Target-fused BERT (TF-BERT) for this task. The former trains a sentence-level opinion term extraction model by BERT, and the target-oriented opinion term is the one nearest to each aspect. The latter utilizes the average pooling of target word embeddings to represent the target information. The word representation at each position is the addition of word embedding and target information, which is fed into BERT to extract target-oriented opinion terms. Zhao et al. (2020) have applied some baselines in AOPE, including HAST (Li et al., 2018) + IOG and JERE-MHS (Bekoulis et al., 2018). Besides the above methods, we also employ the following baselines:
- IOG (Fan et al., 2019) utilizes an Inward-Outward LSTM and a Global LSTM to capture the information of aspects and global information respectively, then it combines these information for sequence labeling.
- SpanMlt (Zhao et al., 2020) is a span-based multi-task learning framework where the terms