A Unified One-Step Solution for Aspect Sentiment Quad Prediction
Year: 2023

Abstract:
Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspectbased sentiment analysis as it provides a complete aspect-level sentiment structure. However, existing ASQP datasets are usually small and low-density, hindering technical advancement. To expand the capacity, in this paper, we release two new datasets for ASQP, which contain the following characteristics: larger size, more words per sample, and higher density. With such datasets, we unveil the shortcomings of existing strong ASQP baselines and therefore propose a unified one-step solution for ASQP, namely One-ASQP, to detect the aspect categories and to identify the aspectopinion-sentiment (AOS) triplets simultaneously. Our One-ASQP holds several unique advantages: (1) by separating ASQP into two subtasks and solving them independently and simultaneously, we can avoid error propagation in pipeline-based methods and overcome slow training and inference in generation-based methods; (2) by introducing sentiment-specific horns tagging schema in a token-pair-based two-dimensional matrix, we can exploit deeper interactions between sentiment elements and efficiently decode the AOS triplets; (3) we design “[NULL]” token can help us effectively identify the implicit aspects or opinions. Experiments on two benchmark datasets and our released two datasets demonstrate the advantages of our One-ASQP. The two new datasets are publicly released at https://www.github. com/Datastory-CN/ASQP-Datasets.

heading: 1 Introduction
text: Aspect-based sentiment analysis (ABSA) is a critical fine-grained opinion mining or sentiment analysis problem that aims to analyze and understand people’s opinions or sentiments at the aspect level (Liu, 2012; Pontiki et al., 2014; Zhang et al., 2022). Typically, there are four fundamental
∗The corresponding author.
sentiment elements in ABSA: (1) aspect category (c) defines the type of the concerned aspect; (2) aspect term (a) denotes the opinion target which is explicitly or implicitly mentioned in the given text; (3) opinion term (o) describes the sentiment towards the aspect; and (4) sentiment polarity (s) depicts the sentiment orientation. For example, given an opinionated sentence, “touch screen is not sensitive,” we can obtain its (c, a, o, s)-quadruple as (“Screen#Sensitivity”, “touch screen”, “not sensitive”, NEG), where NEG indicates the negative sentiment polarity.
Due to the rich usage of applications, numerous research efforts have been made on ABSA to predict or extract fine-grained sentiment elements (Jiao et al., 2019; Pontiki et al., 2014, 2015, 2016; Zhang et al., 2022; Yang et al., 2021). Based on the number of sentimental elements to be extracted, existing studies can be categorized into the following tasks: (1) single term extraction includes aspect term extraction (ATE) (Li and Lam, 2017; He et al., 2017), aspect category detection (ACD) (He et al., 2017; Liu et al., 2021); (2) pair extraction includes aspect-opinion pairwise extraction (AOPE) (Yu et al., 2019; Wu et al., 2020), aspect-category sentiment analysis (ACSA) (Cai et al., 2020; Dai et al., 2020), and
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End-to-End ABSA (E2E-ABSA) (Li et al., 2019b; He et al., 2019) to extract the aspect and its sentiment; (3) triplet extraction includes aspectsentiment triplet extraction (ASTE) (Mukherjee et al., 2021; Chen et al., 2021), and Target Aspect Sentiment Detection (TASD) (Wan et al., 2020); (4) quadruple extraction includes aspectcategory-opinion-sentiment (ACOS) quadruple extraction (Cai et al., 2021) and aspect sentiment quad prediction (ASQP) (Zhang et al., 2021a). ACOS and ASQP are the same tasks, which aim to extract all aspect-category-opinion-sentiment quadruples per sample. Since ASQP covers the whole task name, we use ASQP to denote the ABSA quadruple extraction task. Table 1 summarizes an example of the outputs of various ABSA tasks.
This paper focuses on ASQP because it provides a complete aspect-level sentiment analysis (Zhang et al., 2022). We first observe that existing ASQP datasets are crawled from only one source and are small with low-density (Cai et al., 2021; Zhang et al., 2021a). For example, the maximum sample size is around 4,000, while the maximum number of quadruples per sample is around 1.6. This limits the technical development of ASQP. Second, ASQP includes two extraction subtasks (aspect extraction and opinion extraction) and two classification subtasks (category classification and sentiment classification). Modeling the four subtasks simultaneously is challenging, especially when the quadruples contain implicit aspects or opinions (Cai et al., 2021). Though existing studies can resolve ASQP via pipeline-based (Cai et al., 2021) or generation-based methods (Zhang et al., 2021a; Mao et al., 2022; Bao et al., 2022; Gao et al., 2022), they suffer from different shortcomings, i.e., pipeline-based methods tend to yield error propagation while generation-based methods perform slowly in training and inference.
To tackle the above challenges, we first construct two datasets, en-Phone and zh-FoodBeverage, to expand the capacity of datasets. en-Phone is an English ASQP dataset in the cell phone domain collected from several e-commercial platforms, while zh-FoodBeverage is the first Chinese ASQP dataset collected from multiple sources under the categories of Food and Beverage. Compared to the existing ASQP datasets, our datasets have 1.75 to 4.19 times more samples and a higher quadruple density of 1.3 to 1.8. This achievement is a result of our meticulous definition and adherence to an-
notation guidelines, which allow us to obtain more fine-grained quadruples.
After investigating strong ASQP baselines, we observed a decline in performance on our newly released dataset. This finding, coupled with the shortcomings of the existing baselines, motivated us to develop a novel one-step solution for ASQP, namely One-ASQP. As illustrated in Fig. 1, our One-ASQP adopts a shared encoder from a pretrained language model (LM) and resolves two tasks, aspect category detection (ACD) and aspectopinion-sentiment co-extraction (AOSC) simultaneously. ACD is implemented by a multi-class classifier and AOSC is fulfilled by a token-pair-based two-dimensional (2D) matrix with the sentimentspecific horns tagging schema, a popular technique borrowed from the joint entity and relation extraction (Wang et al., 2020; Shang et al., 2022). The two tasks are trained independently and simultaneously, allowing us to avoid error propagation and overcome slow training and inferring in generationbased methods. Moreover, we also design a unique token, “[NULL]”, appending at the beginning of the input, which can help us to identify implicit aspects or opinions effectively.
Our contributions are three-fold: (1) We construct two new ASQP datasets consisting of more fine-grained samples with higher quadruple density while covering more domains and languages. Significantly, the released zh-FoodBeverage dataset is the first Chinese ASQP dataset, which provides opportunities to investigate potential technologies in a multi-lingual context for ASQP. (2) We propose One-ASQP to simultaneously detect aspect categories and co-extract aspect-opinion-sentiment triplets. One-ASQP can absorb deeper interactions between sentiment elements without error propagation and conquer slow performance in generationbased methods. Moreover, the delicately designed “[NULL]” token helps us to identify implicit aspects or opinions effectively. (3) We conducted extensive experiments demonstrating that One-ASQP is efficient and outperforms the state-of-the-art baselines in certain scenarios.

heading: 2 Datasets
text: We construct two datasets 1 to expand the capacity of existing ASQP datasets.
1More details are provided in Appendix A.

heading: 2.1 Source
text: en-Phone is an English dataset collected from reviews on multiple e-commerce platforms in July and August of 2021, covering 12 cell phone brands. To increase the complexity and the quadruple density of the dataset, we deliver the following filtering steps: (1) applying the LangID toolkit 2 to filter out comments whose body content is not in English; (2) filtering out samples with less than 8 valid tokens. zh-FoodBeverage is the first Chinese ASQP dataset, collected from Chinese comments in multiple sources in the years 2019-2021 under the categories of Food and Beverage. We clean the data by (1) filtering out samples with lengths less than 8 and greater than 4000; (2) filtering out the samples with valid Chinese characters less than 70%; (3) filtering out ad texts by a classifier which is trained by marketing texts with 90% classification accuracy.

heading: 2.2 Annotation
text: A team of professional labelers is asked to label the texts following the guidelines in Appendix A.2. Two annotators individually annotate the same sample by our internal labeled system. The strict quadruple matching F1 score between two annotators is 77.23%, which implies a substantial agreement between two annotators (Kim and Klinger, 2018). In case of disagreement, the project leader will be asked to make the final decision. Some typical examples are shown in Table 10.

heading: 2.3 Statistics and Analysis
text: Table 2 reports the statistics of two existing ASQP benchmark datasets and our released datasets for comparison. en-Phone contains 7,115 samples with 15,884 quadruples while zh-FoodBeverage contains 9,570 samples with 24,973 quadruples. The size and the number of quadruples are significantly larger than the current largest ASQP benchmark dataset, i.e., Laptop-ACOS. The statistics
2https://pypi.org/project/langid/
show that our released datasets contain unique characteristics and are denser than existing RestaurantACOS and Laptop-ACOS: (1) the number of words per sample is 25.78 and 193.95 for en-Phone and zh-FoodBeverage, respectively, while the number of quadruples per sample is 2.23 and 2.61 for enPhone and zh-FoodBeverage accordingly. This shows that en-Phone and zh-FoodBeverage are much denser than existing ASQP datasets; (2) based on the annotation guidelines, we only label opinionated sentences with explicit aspects. Moreover, due to commercial considerations, we exclude sentences with neutral sentiment in zhFoodBeverage; (3) here, we define more finegrained aspects and opinions than existing ASQP datasets; see more examples in Appendix A. Consequently, we attain a longer average length per aspect and per opinion, as reported in the last two columns of Table 2.

heading: 3 Methodology

heading: 3.1 ASQP Formulation
text: Given an opinionated sentence x, ASQP is to predict all aspect-level sentiment quadruples {(c, a, o, s)}, which corresponds to the aspect category, aspect term, opinion term, and sentiment polarity, respectively. The aspect category c belongs to a category set C; the aspect term a and the opinion term o are typically text spans in x while they can be null if the target is not explicitly mentioned, i.e., a ∈ Vx ∪{∅} and o ∈ Vx ∪{∅}, where Vx denotes the set containing all possible continuous spans of x. The sentiment polarity s belongs to one of the sentiment classes, SENTIMENT={POS, NEU, NEG}, which corresponds to the positive, neutral, and negative sentiment, respectively.

heading: 3.2 One-ASQP
text: Our One-ASQP resolves two subtasks, ACD and AOSC, simultaneously, where ACD seeks a classifier to determine the aspect categories, and AOSC is to extract all (a, o, s)-triplets.
Given x with n-tokens, we construct the input as follows:
[NULL]x1 x2 . . . xn, (1)
where the token [NULL] is introduced to detect implicit aspects or opinions; see more details in Sec. 3.2.2. Now, via a pre-trained LM, both tasks share a common encoder to get the representations:
H = hNULL h1 h2 . . . hn ∈ Rd×(n+1), (2)
where d is the token representation size.

heading: 3.2.1 Aspect Category Detection
text: We apply a classifier to predict the probability of category detection:
C = sigmoid(W2(RELU(W1H+ b1))), (3)
where W1 ∈ Rd×d, b1 ∈ Rd , W2 ∈ R|C|×d. Here, |C| is the number of categories in C. Hence, C ∈ R|C|×(n+1), where Cij indicates the probability of the i-th token to the j-th category.

heading: 3.2.2 AOSC
text: We tackle AOSC via a token-pair-based 2D matrix with the sentiment-specific horns tagging schema to determine the positions of aspect-opinion pairs and their sentiment polarity.
Tagging We define four types of tags: (1) ABOB denotes the cell for the beginning position of an aspect-opinion pair. For example, as (“touch screen”, “not sensitive”) is an aspect-opinion pair, the cell corresponding to (“touch”, “not”) in the 2D matrix is marked by “AB-OB”. (2) AEOE indicates the cell for the end position of an aspect-opinion pair. Hence, the cell of (“screen”, “sensitive”) is marked by “AE-OE”. (3) AB-OE*SENTIMENT defines a cell with its sentiment polarity, where the row position denotes the beginning of an aspect and the column position denotes the end of an opinion. Hence, the cell of (“touch”, “sensitive”) is tagged by “AB-OE-NEG”. As SENTIMENT consists of three types of sentiment polarity, there are three cases in AB-OE-*SENTIMENT.
(4) “-” denotes the cell other than the above three types. Hence, we have five types of unique tags, {AB-OB, AE-OE, AB-OE-POS, AB-OE-NEU, ABOE-NEG}.
Triplet Decoding Since the tagged 2D matrix has marked the boundary tokens of all aspectopinion pairs and their sentiment polarity, we can decode the triples easily. First, by scanning the 2D matrix column-by-column, we can determine the text spans of an aspect, starting with “AB-OE-*SENTIMENT” and ending with “AEOE”. Similarly, by scanning the 2D matrix rowby-row, we can get the text spans of an opinion, which start from “AB-OB” and end with “AB-OE*SENTIMENT”. Finally, the sentiment polarity can be easily determined by “AB-OE-*SENTIMENT”.
Implicit Aspects/Opinions Extraction Detecting implicit aspects or opinions is critical in ASQP (Cai et al., 2021). Here, we append the “[NULL]” token at the beginning of the input. Our One-ASQP can then easily determine the cases of Implicit Aspects and Explicit Opinions (IA&EO) and Explicit Aspects and Implicit Opinions (EA&IO). The whole procedure is similar to the above triplet decoding: when the text spans at the row of “[NULL]” start from “AB-OB” and end with “AB-OE-*SENTIMENT”, we can obtain an explicit opinion without aspect. Meanwhile, when the text spans at the column of “[NULL]” start from “AB-OE-*SENTIMENT” and ends with “AE-OE”, we can obtain an explicit aspect without opinion. As shown in Fig. 1, we can quickly obtain the corresponding aspect-opinion pairs as “(NULL, very speedy)” and “(express package, NULL)”. The sentiment polarity can also be determined by “AB-OE*SENTIMENT” accordingly. Although the current setting for IA&IO cannot be solved directly, it is possible to resolve it in two steps. First, we can identify IA&IO using tools such as ExtractClassify-ACOS (Cai et al., 2021). Then, we can classify aspect categories and sentiment polarity. However, a unified solution with One-ASQP is left for future work.
Tagging Score Given H, we compute the probabilities of the (i, j)-th cell to the corresponding tags by:
ai = Wahi + ba, (4)
oj = Wohj + bo, (5)
Pij = sigmoid(aTi oj) ∈ R5 (6)
where Wa ∈ RD×d and Wo ∈ RD×d are the weight matrices for the aspect token and the opinion token, respectively, ba ∈ RD and bo ∈ RD are the biases for the aspect token and the opinion token, respectively. D is the hidden variable size set to 400 as default.

heading: 3.3 Training Procedure
text: Training We train ACD and AOSC jointly by minimizing the following loss function:
Ltotal = αLACD + βLAOSC , (7)
where α and β are trade-off constants set to 1 for simplicity. The ACD loss LACD and the AOSC loss LAOSC are two cross-entropy losses defined as follows:
LACD = − 1
n× |C|× (8)
n∑
i=1
|C|−1∑
j=0
{yCij logCij + (1− yCij) log(1− Cij)},
LAOSC = − 1
(n+ 1)× (n+ 1)× 5× (9) n∑
i=0
n∑
j=0
{Ytij logPij + (1−Ytij) log(1−Pij)},
where Cij is the predicted category computed by Eq. (3), yCij ∈ {0, 1} and it is 1 when the i-th token is assigned to the j-th category and 0 otherwise. Pij is the predicted tagging score computed by Eq. (6) for all five types of tags while Yij ∈ R5 is the ground-truth one-hot encoding.
During training, we implement the negative sampling strategy as (Li et al., 2021) to improve the performance of our One-ASQP on unlabeled quadruples. We set the negative sampling rate to 0.4, a suggested range in (Li et al., 2021) that has yielded good results. Specifically, to minimize the loss in Eq.(7), we randomly sample 40% of unlabeled entries as negative instances, which correspond to ‘0’ in ACD and ‘-’ in AOSC, as shown in Fig.1.

heading: 3.4 Quadruples Decoding
text: After attaining the model, we can obtain the category sequences of ACD and the AOS triplets in the AOSC matrix simultaneously. We then decode the quadruples in one step via their common terms. For example, as shown in Fig. 1, we can merge (Logistics#Speed, express package) and (express package, NULL, POS) via the common aspect term,
“express package”, and obtain the quadruple (Logistics#Speed, express package, NULL, POS).
Overall, our One-ASQP consists of two independent tasks, ACD and AOSC. Their outputs only share in the final decoding stage and do not rely on each other during training as the pipeline-based methods need. This allows us to train the model efficiently and decode the results consistently in both training and test.

heading: 4 Experimental Setup
text: Datasets We conduct the experiments on four datasets in Table 2. For Restaurant-ACOS and Laptop-ACOS, we apply the original splitting on the training, validation, and test sets (Cai et al., 2021). For en-Phone and zh-FoodBeverage, the splitting ratio is 7:1.5:1.5 for training, validation, and test, respectively.
Evaluation Metrics We employ F1 scores as the main evaluation metric and also report the corresponding Precision and Recall scores. A sentiment quad prediction is counted as correct if and only if all the predicted elements are exactly the same as the gold labels. The time cost is also recorded to demonstrate the efficiency of One-ASQP.
Implementation Details One-ASQP is implemented by PyTorch 1.13.1. All experiments are run on a workstation with an Intel Xeon E5-2678 v3@2.50GHz CPU, 256G memory, a single A5000 GPU, and Ubuntu 20.04. For English datasets, we adopt LMs of DeBERTaV3-base and DeBERTaV3large (He et al., 2021), which contain 12 layers with a hidden size of 768 and 24 layers with a hidden size of 1024, respectively. For the Chinese dataset, we adopt MacBERT (Cui et al., 2020), a Chinese LM with the same structure as DeBERTaV3. For the English datasets, the maximum token length is set to 128 as the maximum average word length is only 25.78, as shown in Table 2. For the zhFoodBeverage, the maximum token length is 256. The batch size and learning rate for all experiments are [32, 3e-5] as they can perform well. We monitor the F1 score on the validation set and terminate the training when no score drops for four epochs. Finally, we report the scores on the test set by the best model on the validation set.
Baselines We compare our One-ASQP with strong baselines: (1) pipeline-based methods consist of four methods, i.e., DP-ACOS, JETACOS, TAS-BERT-ACOS, and Extract-Classify-
ACOS, which are all proposed in (Cai et al., 2021); (2) generation-based methods include BART for ABSA (BARTABSA) (Yan et al., 2021), Generative Aspect-based Sentiment analysis (GAS) (Zhang et al., 2021b), Paraphrase generation for ASQP (Zhang et al., 2021a), Seq2Path (Mao et al., 2022), GEN-SCLNAT (Peper and Wang, 2022), and ABSA with Opinion Tree Generation (OTG) (Bao et al., 2022).

heading: 5 Results and Discussions

heading: 5.1 Main Results
text: Table 3 reports the comparison results on two existing ASQP datasets. Since all methods apply the same splitting on these two datasets, we copy the results of baselines from corresponding references. The results show that: (1) Generation-based methods gain significant improvement over pipelinebased methods as pipeline-based methods tend to propagate the errors. (2) Regarding generationbased methods, OTG attains the best performance on the F1 score. The exceptional performance may come from integrating various features, e.g., syntax and semantic information, for forming the opinion tree structure (Bao et al., 2022). (3) Our OneASQP is competitive with generation-based methods. By checking the LM sizes, we know that the generation-based baselines except BARTABSA apply T5-base as the LM, which consists of 220M
parameters. In comparison, our One-ASQP model utilizes DeBERTaV3, which consists of only 86M and 304M backbone parameters for its base and large versions, respectively. The compact model parameter size is a crucial advantage of our approach. However, on the Restaurant-ACOS and Laptop-ACOS datasets, One-ASQP falls slightly behind some generation-based methods that can take advantage of the semantics of sentiment elements by generating natural language labels. In contrast, One-ASQP maps each label to a specific symbol, similar to the numerical indexing in classification models. Unfortunately, the limited quantity of these datasets prevents our One-ASQP model from achieving optimal performance.
We further conduct experiments on en-Phone and zh-FoodBeverage and compare our OneASQP with three strong baselines, Extract-ClassifyACOS, Paraphrase, and GEN-SCL-NAT. We select them because Extract-Classify-ACOS is the best pipeline-based method. Furthermore, Paraphrase and GEN-SCL-NAT are two strong generationbased baselines releasing source codes, which is easier for replication. Results in Table 4 are averaged by five runs with different random seeds and show that our One-ASQP, even when adopting the base LM version, outperforms three strong baselines. We conjecture that good performance comes from two reasons: (1) The newly-released datasets contain higher quadruple density and fine-grained sentiment quadruples. This increases the task difficulty and amplifies the order issue in generationbased methods (Mao et al., 2022), i.e., the orders between the generated quads do not naturally exist, or the generation of the current quads should not condition on the previous ones. More evaluation tests are provided in Sec. 5.4. (2) The number of categories in the new datasets is much larger than Restaurant-ACOS and Laptop-ACOS. This also increases the search space, which tends to yield generation bias, i.e., the generated tokens neither come from the original text nor the pre-defined categories and sentiments. Overall, the results demonstrate the significance of our released datasets for further technical development.
Table 5 reports the time cost (in seconds) of training in one epoch and inferring the models on Restaurant-ACOS and en-Phone; more results are in Appendix B.1. The results show that our OneASQP is much more efficient than the strong baselines as Extract-Classify-ACOS needs to encode
twice and Paraphrase can only decode the token sequentially. To provide a fair comparison, we set the batch size to 1 and show the inference time in the round bracket. The overall results show that our One-ASQP is more efficient than the baselines. Our One-ASQP can infer the quadruples parallel, which is much favorite for real-world deployment.

heading: 5.2 Effect of Handling Implicit Aspects/Opinions
text: Table 6 reports the breakdown performance of the methods in addressing the implicit aspects/opinions problem. The results show that (1) the generationbased baseline, GEN-SCL-NAT, handles EA&IO better than our One-ASQP when the quadruple density is low. Accordingly, One-ASQP performs much better than GEN-SCL-NAT on IA&EO in Restaurant-ACOS. GEN-SCL-NA performs worse in IA&EO may be because the generated decoding space of explicit opinions is huge compared to explicit aspects. (2) In en-Phone and zh-FoodBeverage, One-ASQP consistently outperforms all baselines on EA&EO and EA&IO. Our One-ASQP is superior in handling implicit opinions when the datasets are more fine-grained.

heading: 5.3 Ablation Study on ADC and AOSC
text: To demonstrate the beneficial effect of sharing the encoder for ADC and AOS tasks. We train these two tasks separately, i.e., setting (α, β) in Eq. 7 to (1.0, 0.0) and (0.0, 1.0). The results in Table 7 show that our One-ASQP absorbs deeper information between two tasks and attains better performance. By sharing the encoder and conducting joint training, the connection between the category and other sentiment elements can become more tightly integrated, thereby contributing to each other.

heading: 5.4 Effect of Different Quadruple Densities
text: We conduct additional experiments to test the effect of different quadruple densities. Specifically, we keep those samples with only one quadruple
in en-Phone and zh-FoodBeverage and construct two lower-density datasets, en-Phone (one) and zhFoodBeverage (one). We then obtain 1,528 and 3,834 samples in these two datasets, respectively, which are around one-fifth and two-fifth of the original datasets accordingly.
We only report the results of our OneASQP with the base versions of the corresponding LMs and Paraphrase. Results in Table 8 show some notable observations: (1) Paraphrase can attain better performance on en-Phone (one) than our OneASQP. It seems that generation-based methods are powerful in the low-resource scenario. However, the performance is decayed in the full datasets due to the generation order issue. (2) Our One-ASQP significantly outperforms Paraphrase in zh-FoodBeverage for both cases. The results show that our OneASQP needs sufficient training samples to perform well. However, in zh-FoodBeverage (one), the number of labeled quadruples is 3,834. The effort is light in real-world applications.

heading: 5.5 Error Analysis and Case Study
text: To better understand the characteristics of our OneASQP, especially when it fails. We conduct the error analysis and case study in this section. We check the incorrect quad predictions on all datasets and show one typical error example for each type from Laptop-ACOS in Fig. 2, where we report the percentage of errors for better illustration. The re-
sults show that (1) In general, extracting aspects and opinions tends to introduce larger errors than classifying categories and sentiments. Aspects and opinions have more complex semantic definitions than categories and sentiments, and extracting implicit cases further increases the difficulty of these tasks. (2) There is a significant category error in Laptop-ACOS, likely due to an imbalance issue in which there are 121 categories with relatively small samples per category. For example, 35 categories have less than two quadruples. (3) The percentage of opinion errors is higher than that of aspect errors in all datasets because opinions vary more than aspects, and there are implicit opinions in the new datasets. This is reflected in the numbers of opinion errors in en-Phone and zhFoodBeverage, which are 125 (37.31%) and 395 (49.94%), respectively, exceeding the corresponding aspect errors of 99 (29.55%) and 246 (31.10%). Removing samples with implicit opinions reduces the opinion errors to 102 and 260 in en-Phone and zh-FoodBeverage, indicating that explicit opinion errors are slightly larger than explicit aspect errors. (4) The percentage of sentiment errors is relatively small, demonstrating the effectiveness of our proposed sentiment-specific horns tagging schema.

heading: 6 Related Work
text: ABSA Benchmark Datasets are mainly provided by the SemEval’14-16 shared challenges (Pontiki et al., 2014, 2015, 2016). The initial task is only to identify opinions expressed about specific entities and their aspects. In order to investigate more tasks, such as AOPE, E2E-ABSA, ASTE, TASD, and ASQP, researchers have re-annotated the datasets and constructed some new ones (Fan et al., 2019; Li
et al., 2019a; Xu et al., 2020; Wan et al., 2020; Cai et al., 2021). However, re-annotated datasets still contain the following limitations: (1) The data is collected from only one source, limiting the scope of the data; (2) the data size is usually small, where the maximum one is only around 4,000; (3) there is only a labeled quadruple per sentence and many samples share a common aspect, which makes the task easier; (4) the available public datasets are all in English. The shortcomings of existing benchmark datasets motivate us to crawl and curate more data from more domains, covering more languages and with higher quadruple density. ASQP aims to predict the four sentiment elements to provide a complete aspect-level sentiment structure (Cai et al., 2021; Zhang et al., 2021a). The task is extended to several variants, e.g., capturing the quadruple of holder-target-expression-polarity (R et al., 2022; Lu et al., 2022) or the quadruple of target-aspect-opinion-sentiment in a dialogue (Li et al., 2022). Existing studies can be divided into the pipeline or generation paradigm. A typical pipeline-based work (Cai et al., 2021) has investigated different techniques to solve the subtasks accordingly. It consits of (1) first exploiting double propagation (DP) (Qiu et al., 2011) or JET (Xu et al., 2020) to extract the aspect-opinion-sentiment triplets and after that, detecting the aspect category to output the final quadruples; (2) first utilizing TAS-BERT (Wan et al., 2020) and the ExtractClassify scheme (Wang et al., 2017) to perform the aspect-opinion co-extraction and predicting category-sentiment afterward. Most studies fall in the generation paradigm (Zhang et al., 2021a; Mao et al., 2022; Bao et al., 2022; Gao et al., 2022). Zhang et al. (2021a) is the first generationbased method to predict the sentiment quads in an end-to-end manner via a PARAPHRASE model-
ing paradigm. It has been extended and overcome by Seq2Path (Mao et al., 2022) or tree-structure generation (Mao et al., 2022; Bao et al., 2022) to tackle the generation order issue or capture more information. Prompt-based generative methods are proposed to assemble multiple tasks as LEGO bricks to attain task transfer (Gao et al., 2022) or tackle few-shot learning (Varia et al., 2022). GENSCL-NAT (Peper and Wang, 2022) is introduced to exploit supervised contrastive learning and a new structured generation format to improve the naturalness of the output sequences for ASQP. However, existing methods either yield error propagation in the pipeline-based methods or slow computation in the generation-based methods. The shortcomings of existing methods motivate us to propose One-ASQP.

heading: 7 Conclusions
text: In this paper, we release two new datasets, with the first dataset being in Chinese, for ASQP and propose One-ASQP, a method for predicting sentiment quadruples simultaneously. One-ASQP utilizes a token-pair-based 2D matrix with sentiment-specific horns tagging, which allows for deeper interactions between sentiment elements, enabling efficient decoding of all aspect-opinion-sentiment triplets. An elaborately designed “[NULL]" token is used to identify implicit aspects or opinions effectively. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of One-ASQP. Notably, existing strong baselines exhibit a decay in performance on the newly released datasets. We hope these datasets and One-ASQP will inspire further technical development in this area.

heading: Acknowledgments
text: The work was partially supported by the IDEA Information and Super Computing Centre (ISCC) and the National Nature Science Foundation of China (No. 62201576).
Limitations
Our proposed One-ASQP still contains some limitations:
• Our One-ASQP does not solve the case of IA&IO. We defer the technical exploration of this issue to future work. • One-ASQP has to split the ASQP task into two subtasks, ADC and AOSC. It is still promising to explore more effective solutions, e.g., by only one task, which can absorb deeper interactions between all elements. • Generally, One-ASQP suffers more opinion errors than other sentiment elements due to the fine-grained annotation and implicit opinions issues. It is possible to tackle it by exploring more advanced techniques, e.g., syntax or semantics augmentation, to dig out deeper connections between options and other sentiment elements. • One-ASQP tends to make errors when there are many aspect categories with small labeled quadruples. It is also significant to explore more robust solutions to detect the aspect categories in the low-resource scenario. • Though we have released datasets in both English and Chinese, we do not explore ASQP in the multi-lingual scenario. We leave this as future work.
Ethics Statement
We follow the ACL Code of Ethics. In our work, there are no human subjects and informed consent is not applicable.

heading: A More Details about Datasets Construction
text: This section provides more details about constructing the two datasets, en-Phone and zhFoodBeverage.
A.1 Data Sources
The English ASQP dataset, en-Phone, is collected from reviews on Amazon UK 3, Amazon India 4 and Shopee 5 in July and August of 2021, covering 12 cell phone brands, such as Samsung, Apple, Huawei, OPPO, Xiaomi, etc.
The first Chinese ASQP dataset, zhFoodBeverage, is collected from the Chinese comments on forums 6, Weibo 7, news 8 and e-commerce platforms 9 in the years 2019-2021 under the categories of Food and Beverage.
A.2 Annotation Guidelines
The following outlines the guidelines for annotating the four fundamental sentiment elements of ASQP and their outcomes. It can be noted that our labeled ASQP quadruples are more fine-grained and more difficult than those in existing ASQP benchmark datasets.
A.2.1 Aspect Categories The aspect category defines the type of the concerned aspect. Here, we apply a two-level category system, which is defined by our business experts for the sake of commercial value and more detailed information. For example, “Screen” is a first-level category. It can include secondlevel categories, such as “Clarity”, “General”,
3https://www.amazon.co.uk/ 4https://www.amazon.in/ 5https://shopee.com.my/ 6http://foodmate.net/ 7https://weibo.com/ 8https://chihe.sohu.com/ 9https://www.jd.com/,https://www.taobao.com/
and “Size”, to form the final second-level categories as “Screen#Clarity”, “Screen#General”, and “Screen#Size”. In the experiments, we only consider the second-level categories.
As reported in Table 2, the number of categories for en-Phone and zh-FoodBeverage is 88 and 138, respectively. The number of labeled quadruples per category is larger than 5. Though Laptop-ACOS consists of 121 categories, if we filter out the categories with less than 5 annotated quadruples, the number of categories is reduced to 75. Hence, we provide more dense and rich datasets for ASQP.
A.2.2 Aspect Terms The aspect term is usually a noun or a noun phrase, indicating the opinion target, in the text. It can be implicit in a quadruple (Cai et al., 2021). For the sake of commercial analysis, we exclude sentences without aspects. Moreover, to provide more finegrained information, we include three additional rules:
• The aspect term can be an adjective or verb when it can reveal the sentiment categories. For example, as the example of en-Phone in Table 10, “recommended” is also labeled as an aspect in “Highly recommended” because it can identify the category of “Buyer_Atitude#Willingness_Recommend”. In Ex. 1 and Ex. 4 of Table 9, “clear” and “cheap” are labeled as the corresponding aspect terms because they can specify the category of “Screen#Clarity” and “Price#General”, accordingly. • Pronoun is not allowed to be an aspect term as it cannot be identified by the quadruples only. For example, in the example of “pretttyyyy and affordable too!!! I love it!! Thankyouuu!!”, “it” cannot be labeled as the aspect though we know it indicates a phone from the context. • Top-priority in labeling fine-grained aspects. For example, in the example of “Don’t purchase this product”, “purchase” is more related to a customer’s purchasing willingness while “product” is more related to the overall comment, we will label “purchase” as the aspect.
A.2.3 Opinion Terms The opinion term describes the sentiment towards the aspect. An opinion term is usually an adjective or a phrase with sentiment polarity. Here, we
include more labeling criteria:
• When there is a negative word, e.g., “Don’t”, “NO”, “cannot”, “doesn’t”, the negative word should be included in the opinion term. For example, “not large” and “Don’t like” are labeled as the corresponding opinion terms in Ex. 1 and Ex. 2 of Table 9 • When there is an adverb or a preposition, e.g., “very”, “too”, “so”, “inside”, “under”, “outside”, the corresponding adverb or preposition should be included in the opinion term. For example, in Ex. 3 of Table 9, “Very fast” is labeled as an opinion term. Usually, in Restaurant-ACOS and Laptop-ACOS, “Very” is not included in the opinion term. Moreover, in Ex. 1 of Table 9, “very” in “very clear” is labeled as an opinion term while in Ex. 4, “very” in “very cheap” is labeled as the opinion term.
These examples show that our labeled opinion terms are more fine-grained and complicated, but more valuable for real-world applications. This increases the difficulty of extracting opinion terms and demonstrates the significance of our released datasets to the ASBA community.
A.2.4 Sentiment Polarity The sentiment polarity belongs to one of the sentiment classes, {POS, NEU, NEG}, for the positive, neutral, and negative sentiment, respectively. In zh-FoodBeverage, for commercial considerations, we only label sentences with positive and negative sentiments and exclude those with neutral sentiment.
A.3 Quadruple Density Analysis
For better illustration, we count the number
of quadruples per sentence in four datasets and show the ratios in Fig. 3. It is shown that (1) In terms of sentences with at most one labeled quadruple, Restaurant-ACOS contains 61.12% of the sentences and it is 71.54% in Laptop-ACOS. Meanwhile, it is 39.33% and 39.10% in en-Phone and zh-FoodBeverage, respectively. (2) In terms of sentences with at least three labeled quadruples, it drops significantly to 14.09% in RestaurantACOS and 8.34% in Laptop-ACOS. Meanwhile, it is 35.19% in en-Phone and 34.01% in zhFoodBeverage. Hence, our released datasets are more dense and balanced.

heading: B More Experimental Results
text: B.1 Computation Efficiency Table 11 reports the time cost (in seconds) on all four datasets. The base versions of the corresponding LMs are applied in Extract-Classify. It shows that One-ASQP is efficient in both training and inference, which is a favorite for real-world deployment.
B.2 Effect of Variants of Interactions Though our One-ASQP separates the task into ACD and AOSC. There are still other variants to resolve the ASQP task. Here, we consider two variants:
Variant 1: The ASQP task is separated into three sub-tasks: aspect category detection (ACD), aspect-opinion pair extraction (AOPC), and sentiment detection. More specifically, ACD and sentiment detection are fulfilled by classification models. For AOPC, we adopt the sentiment-specific
horns tagging schema proposed in Sec. 3.2.2. That is, we only co-extract the aspect-opinion pairs. In the implementation, we set the tags of AB-OE*SENTIMENT to AB-EO and reduce the number of tags for AOSC to three, i.e., {AB-OB, AE-OE, AB-OE}.
Variant 2: We solve the ASQP task by a unified framework. Similarly, via the sentiment-specific horns tagging schema proposed in Sec. 3.2.2, we extend the tags of AB-EO-*SENTIMENT to AB-OE-*SENTIMENT-*CATEGORY. Hence, the number of tags increases from 5 to 2 + |S| ∗ |C|, where |S| is the number of sentiment polarities and |C| is the number of categories. This setting allows us to extract the aspect-opinion pairs via the 2D matrix while decoding the categories and sentiment polarities via the tags.
Tabel 12 reports the compared results on four datasets, where the base versions of the corresponding LMs are applied. The results show that (1) our One-ASQP performs the best over the proposed two variants. We conjecture that the aspect-opinionsentiment triplets are in a suitable tag space and our One-ASQP can absorb their interactions effectively. (2) Variant 2 performs the worst among all results. We conjecture that the search tag space is too large and the available datasets do not contain enough information to train the models.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Limitations

heading: 3 A2. Did you discuss any potential risks of your work?
text: Limitations
3 A3. Do the abstract and introduction summarize the paper’s main claims? 0, 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? 2, A
3 B1. Did you cite the creators of artifacts you used? 1, 4
3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? 0

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 4

heading: 3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? 2, A

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? 2, A
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 2, 4
C 3 Did you run computational experiments? 4

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? 4
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? 4

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 2, 4
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? 2, A

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? A

heading: 3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? A

heading: 3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to
text: crowdworkers explain how the data would be used? 2
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. Left blank.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Not applicable. Left blank.

heading: Future Work
text: We defer the technical exploration of this issue to future work. • One-ASQP has to split the ASQP task into two subtasks, ADC and AOSC. It is still promising to explore more effective solutions, e. g. , by only one task, which can absorb deeper interactions between all elements. • Generally, One-ASQP suffers more opinion errors than other sentiment elements due to the fine-grained annotation and implicit opinions issues. It is possible to tackle it by exploring more advanced techniques, e. g. , syntax or semantics augmentation, to dig out deeper connections between options and other sentiment elements. • One-ASQP tends to make errors when there are many aspect categories with small labeled quadruples. It is also significant to explore more robust solutions to detect the aspect categories in the low-resource scenario. • Though we have released datasets in both English and Chinese, we do not explore ASQP in the multi-lingual scenario. We leave this as future work