A Unified One-Step Solution for Aspect Sentiment Quad Prediction (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 .

## introduction 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 @xcite @xcite . typically, there are four fundamental task output example output ate {a} {touch screen} acd {c} {screen#sensitivity} aope {(a, o)} {(touch screen, not sensitive)} acsa {(c, s)} {(screen#sensitivity, neg)} e2e-absa {(a, s)} {(touch screen, neg)} aste {(a, o, s)} {(touch screen, not sensitive, neg)} tasd {(c, a, s)} {(screen#sensitivity, touch screen, neg)} asqp/ acos {(c, a, o, s)} {(screen#sensitivity, touch screen, not sensitive, neg)} this paper focuses on asqp because it provides a complete aspect-level sentiment analysis @xcite . we first observe that existing asqp datasets are crawled from only one source and are small with low-density @xcite . 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 @xcite . though existing studies can resolve asqp via pipeline-based @xcite or generation-based methods @xcite @xcite , 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 @xcite . 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. ## datasets we construct two datasets foot_0 to expand the capacity of existing asqp datasets. (3) filtering out ad texts by a classifier which is trained by marketing texts with 90% classification accuracy. ## experimental setup datasets we conduct the experiments on four datasets in 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 @xcite , 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 @xcite , 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 baselines we compare our one-asqp with strong baselines: (1) pipeline-based methods consist of four methods, i.e., dp-acos, jet-acos, tas-bert-acos, and extract-classifyacos, which are all proposed in @xcite ; (2) generation-based methods include bart for absa (bartabsa) @xcite , generative aspect-based sentiment analysis (gas) @xcite , paraphrase generation for asqp @xcite , seq2path @xcite , gen-scl-nat @xcite , and absa with opinion tree generation (otg) @xcite . we further conduct experiments on en-phone and zh-foodbeverage and compare our one-asqp with three strong baselines, extract-classify-acos, 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 ## conclusions 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.