MVP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
Year: 2023

Abstract:
Generative methods greatly promote aspectbased sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity of language expression on the results. In this work, we propose Multi-view Prompting (MVP) that aggregates sentiment elements generated in different orders, leveraging the intuition of human-like problem-solving processes from different views. Specifically, MVP introduces element order prompts to guide the language model to generate multiple sentiment tuples, each with a different element order, and then selects the most reasonable tuples by voting. MVP can naturally model multi-view and multi-task as permutations and combinations of elements, respectively, outperforming previous task-specific designed methods on multiple ABSA tasks with a single model. Extensive experiments show that MVP significantly advances the state-of-the-art performance on 10 datasets of 4 benchmark tasks, and performs quite effectively in low-resource settings. Detailed evaluation verified the effectiveness, flexibility, and cross-task transferability of MVP.1

heading: 1 Introduction
text: Aspect-based sentiment analysis (ABSA) aims to predict tuples of sentiment elements of interest for a given text. There are four sentiment elements that constitute the main line of ABSA research: aspect term (a), aspect category (c), opinion term (o) and sentiment polarity (s) (Zhang et al., 2022). Given an example sentence, “I love the sushi badly!”, the corresponding elements are “sushi”, “food quality”,
∗Equal contribution. †Corresponding author.
1Code and data released at https://github.com/ ZubinGou/multi-view-prompting
“love” and “positive”, respectively. Early studies focus on a single sentiment element like aspect term (Liu et al., 2015; Ma et al., 2019), aspect category (Zhou et al., 2015) or sentiment polarity (Wang et al., 2016; Chen et al., 2017). Recent works propose compound ABSA tasks involving multiple associated elements, such as aspect sentiment triplet extraction (ASTE) (Peng et al., 2020), target aspect sentiment detection (TASD) (Wan et al., 2020), aspect sentiment quad prediction (ASQP) (Zhang et al., 2021a) and aspect category opinion sentiment (ACOS) (Cai et al., 2020a). Their target formats are shown in Table 1.
Recently, generative methods have been used to handle various ABSA tasks uniformly and achieved good performance (Zhang et al., 2022), where the common practice is to generate a sequence of sentiment elements in a specified format to leverage label semantics. To be specific, they use class index (Yan et al., 2021), sentiment element sequence (Zhang et al., 2021d), natural language (Liu et al., 2021a; Zhang et al., 2021b), structured extraction schema (Lu et al., 2022b) or opinion tree (Bao et al., 2022) as the target of the generation models.
However, previous works usually generate the sequence of sentiment elements in a left-to-right fixed order, which ignores the influence of the interdependence of the elements in a sentiment tuple
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and the diversity of language expression on the targets. For example, the “c ⇒ s ⇒ a ⇒ o” order in PARAPHRASE (Zhang et al., 2021b) (Figure 1). This single-order generation has the following potential drawbacks: (1) Incompleteness, tuple prediction is not naturally a text generation task, the relationship among elements is not ordered but interdependent; (2) Instability, as shown in a study by Hu et al. (2022), the performance of different target template orders differs significantly; (3) Error accumulation, the previous prediction errors will be accumulated and affect later predictions.
To address the above challenges, we propose Multi-view Prompting (MVP) that aggregates sentiment elements predicted in different orders, leveraging the intuition of solving problems from different views in human reasoning and decision (Stanovich and West, 2000). Inspired by prompt chaining (Liu et al., 2021b; Wei et al., 2022b; Wang et al., 2022b,a), MVP introduces element orderbased prompt learning to control the prediction order of sentiment elements, enabling diverse target expressions. Compared to single-order generation, MVP mitigates the incompleteness and instability of a fixed order by receiving information from multiple views, while alleviating the potential error accumulation of generative methods via permutation of elements (Figure 1). Besides, MVP is naturally suited for training a single model to solve multiple ABSA tasks as combinations of elements, adaptively enabling knowledge transfer from related
tuple prediction tasks. We conduct extensive experiments on main aspect sentiment tuple prediction tasks, including ASQP, ACOS, ASTE and TASD. Empirical results show the superiority of MVP in supervised, lowresource, and cross-task transfer settings. In supervised settings, the single-task and multi-task MVP outperform the state-of-the-art by 1.34% and 1.69% absolute F1 scores on all tasks, respectively. At low resource settings, MVP has sizable improvement over strong baselines, and cross-task transfer brings a more remarkable improvement.
Our major contributions are as follows: 1) We introduce MVP, an element order-based prompt learning method that improves sentiment tuple prediction by aggregating multi-view results.
2) MVP naturally allows us to train a single model simultaneously on all tasks. To the best of our knowledge, the multi-tasking MVP is the first single model that substantially outperforms task-specific models on various ABSA tasks.
3) Experiments show that MVP significantly advances the state-of-the-art on 10 datasets of 4 tasks and is quite effective in low-resource settings.

heading: 2 Methodology
text: To better understand the operation process of the proposed MVP, we can carefully observe the pipeline shown in Figure 1. Unlike the fixed order element prediction adopted by previous methods like PARAPHRASE (Zhang et al., 2021b), we take
every possible permutation of sentiment elements (6 for the triplet and 24 for the quadruplet) into account and select the appropriate subsets of them for efficiency and effectiveness reasons. Conditioned on different ordered prompts, a model can generate multiple tuples from different views. Some views give the same correct tuples, while some views are less effective and thus might be wrong, but it’s unlikely to result in the same error. In other words, different views tend to show more agreement in the correct sentiment tuples. Following this intuition, the proposed MVP aggregates and takes the tuples that most views agree on as the final result.

heading: 2.1 Problem Definition
text: In this section, we present our approach with the quadruple task by default, which can be applied to triplet tasks with minor modifications. We formally define the task as follows:
Given an input sentence, aspect sentiment tuple prediction aims to predict all sentiment tuples T = {(a, c, o, s)}, each consisting of aspect term (a), aspect category (c), opinion term (o) and sentiment polarity (s). To leverage label semantics, following previous works (Zhang et al., 2021a), we paraphrase these elements to natural language ea, ec, eo, es separately. For example, we map the “POS” label of sentiment polarity s to “great”, and map the “NULL” label of opinion term o to “it”.

heading: 2.2 Element Order-based Prompt Learning
text: To control the prediction order of sentiment elements, MVP introduces an element order-based prompting mechanism. Specifically, we design the target with ordered target schema and input with element order prompts.

heading: 2.2.1 Ordered Target Schema
text: To indicate different sentiment elements, we follow the DLO method (Hu et al., 2022) and design element markers to represent the structure of the information (Paolini et al., 2021). The element markers for ea, ec, eo, and es are [A], [C], [O] and
[S], respectively. We add the corresponding marker as a prefix to each element and concatenate them in a given permutation pi as the target sequence, for example, “[O]eo[A]ea[C]ec[S]es”.
If there are multiple sentiment tuples for an input sentence, we utilize a special symbol [SSEP] to concatenate their corresponding ordered target schema to get the final target sequence ypi .

heading: 2.2.2 Element Order Prompts
text: We design element order prompts by concatenating these element markers to represent the desired order pi of sentiment elements (for example, “[O][A][C][S]” indicates prediction in the order of “o ⇒ a ⇒ c ⇒ s”). Then, we add the prompt as a suffix to each input sentence to get the final input xpi . Thus we obtain an input-output pair for training:
Input (x): I love the sushi badly! [O][A][C][S] Output (y): [O] love [A] sushi [C] food [S] great We find that the design of element order prompts can effectively guide sentiment tuples’ generation order. Thus multi-view and multi-task can be flexibly modeled through the permutation and combination of elements.

heading: 2.3 Multi-view Training
text: For training, MVP selects appropriate element orders to construct input-target pairs, and then finetunes a Seq2Seq model.

heading: 2.3.1 Element Order Selection
text: Since overheads increase linearly with the number of views and the performance of different views varies, we need to select appropriate element orders. Following the study of prompt ordering (Lu et al., 2022a; Hu et al., 2022), we choose the potentially better-performing orders based on the average entropy of the candidate permutations on the training set. The steps are as follows: (i) we use every possible permutation pi of sentiment elements as candidates; (ii) given an input sentence x and its target tuples, we construct the ordered target schema
ypi of permutation pi as described in §2.2.1, replace the element markers in it with spaces to avoid noises, and query a pre-trained language model to get conditional generation scores p(ypi |x); and (iii) calculate the average score of permutation pi over the training set D:
Spi =
∑ D p(ypi |x)
|D| (1)
Thus we can rank each permutation pi with Spi and top m permutations are used for training.

heading: 2.3.2 Training
text: With the selected m permutations, we construct m different ordered prompts and targets for each sentence. Given the input-target pair (x,y), we can fine-tune a pre-trained sequence-to-sequence language model (LM) such as BART (Lewis et al., 2020) or T5 (Raffel et al., 2020), minimizing the following negative log-likelihood loss:
LNLL = −E log p(y|x)
= −E T∑
t=1
log p(yt|x,y<t) (2)
where T is the length of the target sequence y and y<t denotes previously generated tokens.

heading: 2.4 Multi-view Inference
text: For inference, MVP prompts the trained model to normatively generate multiple sentiment tuples in previously selected orders, and finally aggregate to obtain the most reasonable tuples.

heading: 2.4.1 Schema Constrained Generation
text: Given an input sentence, we construct multiple prompts in the same order as the training, which guides the model to generate targets from different views. However, the generated results may not conform to the target schema format, especially when the training set is small (Zhang et al., 2021a; Yan et al., 2021). Therefore, we designed a schemabased constrained decoding (Cao et al., 2021) that injects target schema knowledge into the decoding process. It ensures that the generated elements are in the corresponding vocabulary set. See Appendix B for implementation details.

heading: 2.4.2 Multi-view Results Aggregation
text: Since each view may predict more than one tuple, we first aggregate the results of all views and then use the tuples that appear in most views as the final
prediction. Specifically, for an input sentence x, suppose we prompt a trained model to generate from m selected permutations, and the set of predicted tuples for permutation pi is T ′pi , which may contain one or more sentiment tuples, and then we can obtain the final aggregated result T ′MVP by the following equation:
T ′MVP = {t|t ∈ m⋃
i=1
T ′pi and ( m∑
i=1
1T ′pi (t) ≥ m 2 )}

heading: 3 Experiments

heading: 3.1 Tasks and Dataset
text: We validate our methods on 10 datasets over 4 tasks, including quadruplet tasks, ASQP and ACOS, and triplet tasks, ASTE and TASD. For a fair comparison, we apply the same data splits as previous works. The targets of each task are shown in Table 1 and the detailed statistics are in Appendix A.
For the ASQP task, we adopt two datasets in the restaurant domain based on SemEval tasks (Pontiki et al., 2015, 2016), Rest15 and Rest16 aligned and completed by Zhang et al. (2021a) subsequently. For the ACOS task, we apply Restaurant-ACOS and Laptop-ACOS constructed by Cai et al. (2021). Compared with ASQP, datasets of ACOS focus on implicit aspects and opinions, which helps to measure our methods comprehensively. For the triple tasks, we adopt datasets provided by Xu et al. (2020) and Wan et al. (2020) for ASTE (Peng et al., 2020) and TASD, respectively.

heading: 3.2 Implement Details
text: We employ T5-BASE model (Raffel et al., 2020) from Huggingface Transformers library2(Wolf et al., 2020) as the pre-trained model. T5 adopts a classical encoder-decoder architecture similar to Transformer (Vaswani et al., 2017). We use greedy search for decoding by default. We use the same hyperparameters across all tasks and datasets, and detailed settings can be found in Appendix C.
The number of views m is set to 5 by default across the majority of the experiments, including multi-task, low-resource, cross-task transfer, and ablations. Only the single-task model in the main experiment uses 15 views for the quadruplet tasks and 5 views for the triplet tasks. For simplicity, the number of views in inference is the same as that in
2https://github.com/huggingface/transformers
training. The case of using a different number of views is left for further exploration.
In the multi-task settings, to introduce domain information, we simply add the task name and dataset name followed by colon separators (e.g. “ASQP: Rest15: ”) as the prefixes to each input sentence, and train a single model on all datasets across all tasks (ASQP, ACOS, TASD, ASTE). We select the appropriate element orders for each dataset separately. We find overlap between the input sentences across different splits of different datasets. Therefore, to avoid data leakage, we collect the training sets from all datasets, and discard samples that overlap with the test set of any task. Then we split the data by 9:1 to obtain the final training and validation sets for our multi-tasking method.

heading: 3.3 Evaluation Metrics
text: For all ABSA tasks, a predicted sentiment tuple is considered as correct if and only if all its elements are exactly the same as the gold tuple. We use F1 scores as the main evaluation metrics (Zhang et al., 2021a; Mao et al., 2022). All reported F1 scores are averaged over 5 runs with different random seeds. For multi-task settings, we use a different split of the training and development sets in each run.

heading: 3.4 Compared Methods
text: We compare our methods with the following three types of previous state-of-the-art methods:
Discriminative methods. TAS-BERT, based on extraction, (Wan et al., 2020) jointly detects the sentiment tuples. Extract-Classify (Cai et al., 2021) decomposes the ACOS task into two steps.
For ASTE, Jet-BERT (Xu et al., 2020) addresses the task in an end-to-end framework by a tagging scheme.
Generative methods. GAS (Zhang et al., 2021c) is the first to model ABSA tasks as a generation process. Paraphrase (Zhang et al., 2021a) designs semantic templates filled with fixed-order elements of tuples as generation targets. Seq2Path (Mao et al., 2022) generates tuples as paths of a tree and then selects valid ones. DLO / ILO (Hu et al., 2022) augments ASQP dataset given the order-free property of the quadruplet based on templates. We also consider UIE (Lu et al., 2022b), a unified textto-structure framework to model various IE tasks which is pre-trained on large-scale data.
Multi-tasking methods. A recent trend is tackling multiple ABSA tasks uniformly using a single multi-tasking model. LEGO-ABSA (Gao et al., 2022) designs task prompts similar to T5 and UnifiedABSA (Wang et al., 2022c) adopts instruction tuning (Mishra et al., 2022; Wei et al., 2022a).
As a fair comparison, all results of these supervised methods are obtained from the base pretrained model, either BERT or T5.
Large language model (LLM). To compare our method with advanced large language models, we additionally include evaluation results of ChatGPT3 (gpt-3.5-turbo) on the four ABSA tasks with zero- and few-shot prompts. The results can be found in Appendix D.
3https://chat.openai.com/

heading: 4 Results and Discussions

heading: 4.1 Single-task and Multi-task Results
text: Our methods outperform previous best baselines significantly in supervised settings among 4 tasks, 10 datasets, becoming the new state-of-the-art in all of them. As shown in Table 2, we observe that:

heading: 1) By aggregating results from multiple views,
text: MVP surpasses the most of previous single-order methods. In comparison with Paraphrase, the single-view method which applies templates with elements in a fixed order, MVP achieves a sizable improvement of 1.89% on average, verifying the effectiveness of multiple informative views.

heading: 2) Element order-based prompt learning effectively guides the generation of tuples by unifying
text: training and inference. Compared with DLO augmenting data on the target side, MVP obtains an improvement of 1.34% by generating tuples control-
lably with designed element order-based prompts. 3) MVP can be applied without abundant pretraining simply and achieves better performance. It is notable that MVP trained on T5-base exceeds UIE using T5-v1.1-base and subsequently trained on a large corpus with 65M instances on all datasets of ASTE (+1.00 on average).
Multi-task Learning. By permutation and combination, MVP (multi-task) obtains generalized ability among diverse tasks. Compared with LEGO-ABSA, a multi-task unified baseline, MVP (multi-task) obtains a +2.82% absolute improvement in F1 score on average.
MVP, a single model completing tuple prediction by information from multiple views, can be naturally employed on multiple ABSA tasks, achieving competitive or better performances than previous methods that require task-specific fine-tuning, data augmentation (e.g., Seq2Path) or complex pre-
training (e.g., UIE), either in single-task or multitasking settings, showing strong stability.

heading: 4.2 Low-resource Results
text: To further explore the behavior of our methods in low-resource settings, we train Paraphrase, DLO, MVP using 1%, 2%, 5%, 10%, and 20% of 4 different training sets in 2 domains over 4 tasks. The F1 scores of test sets are reported in Table 3. We find that MVP, with efficient prompts from different views, achieves better results than previous works with only a small number of samples. In particular, MVP outperforms Paraphrase and DLO substantially in all settings with a performance boost of 5.70% and 3.87% F1 on average.
Cross-task transfer. Based on MVP, transfer brings further significant improvements, from triplets to quadruplets and vice versa. MVP (transfer) performs relatively well in extremely lowresource situations, thus exceeding strong baselines under cross-task transfer situations, both in-domain and cross-domain. Compared with DLO (transfer), MVP (transfer) achieves considerably better results under various transfer settings, showing a strong transferability (from 50.36% to 68.78% for TASD). Rather than capture task-specific features, MVP effectively shares ABSA abilities. MVP (transfer) trained with simple tasks (TASD and ASTE here) with adequate data can be easily transferred to tough tasks (ASQP and ACOS here) when the dataset sizes are small, and vice versa.

heading: 4.3 Effect Analysis
text: Effect of the number of views. MVP raises a question that how many views should be selected for training and inference, which we further explore by varying the number of views and the size of the training set (Figure 3). As the number of views increases, curves show an ascending trend first. Interestingly, when the resource is adequate, F1 decreases slightly after a certain number (between 7 and 15). We believe views with lower ranks may be less effective. Thus it is crucial to balance the size of the data and the number of views. It is dramatic that MVP performs decently even with a single view, probably due to the appropriate order selection and the constrained decoding. In extremely low-resource scenarios, by setting a larger value, MVP can expand single-view information and provide more potential choices. The maximum number of views for quadruplets is much higher than that for triples, making MVP more appropriate for quadruple tasks. We provide further comparisons with single-view prompting (i.e., selecting the best single view for training and inference) on all tasks in the Appendix E.
Effect of aggregation strategy. To explore the effect of different aggregation strategies, we conduct ablation studies mainly on ASTE and ASQP tasks, as shown in Table 4. We can see that replacing majority voting with random selection or ranking results in a reduction of F1 in most cases, indicating that majority voting is a more stable strategy for handling diverse views.
Effect of constrained decoding. The designed constrained decoding guides the generation of different views by limiting the predicted term to a specific list. The impact of this algorithm increases
Example 1 (ASQP task)
Sentence: The restaurant offers an extensive wine list and an ambiance you won’t forget.
Gold: (wine list, drinks style_options, great, extensive), (ambiance, ambience general, great, won’t forget)
Tuples from 15 views: (wine list, drinks style_options, great, extensive) * 10 (ambiance, ambience general, great, won’t forget) * 15 (restaurant, drinks style_options, great, extensive) * 5
Final output: (wine list, drinks style_options, great, extensive) (ambiance, ambience general, great, won’t forget)
Example 2 (ACOS task)
Sentence: I do like the screen on this , images are clean and crisp , enjoying the 4 gigs of ram which allow me to have a few more tab open .
Gold: (screen, display general, great, like), (ram, memory general, great, enjoying), (screen, display general, great, clean), (screen, display general, great, crisp)
Tuples from 15 views: (screen, display general, great, like) * 15 (ram, memory general, great, enjoying) * 9 (images, display general, great, clean) * 8 (images, display quality, great, crisp) * 8
Final output: (screen, display general, great, like) (ram, memory general, great, enjoying) (images, display general, great, clean) (images, display quality, great, crisp)
(images, display quality, great, clean) * 6 (ram, memory operation_performance, great, enjoying) * 6 (images, display general, great, crisp) * 6 (images, display design_features, great, clean) * 1 (images, display design_features, great, crisp) * 1
✓ ✓
✓ Pick
Drop
✘ ✘
✓
Pick
Drop
Figure 4: Two examples including the input sentence, quadruplets or triples predicted, and the final outputs of MVP after filtering by voting. Pick means that the tuple has appeared in more than half of the predictions in multiple views, while drop means that it has appeared less than half of the times and is discarded. Words in green are positive ones while those in red are wrongly picked. Tuples in orange are the ones that MVP ignores.
as the size of the data decreases, and in extremely low-resource scenarios, MVP combined with this algorithm performs considerably well (Table 4).

heading: 4.4 Case Study & Error analysis
text: Figure 4 shows two examples in Rest16 ASQP and Laptop-ACOS, respectively. It can be observed from Example 1 that MVP handles cases with multiple sentiment tuples in a sentence well after filtering unreasonable tuples predicted, i.e. (restaurant, drinks style_options, great, extensive), appearing in five generated results in the case. MVP only outputs tuples considered important in most views and thus repairs the error in the single view by receiving and aggregating information from multiple views. In Example 2, the challenging Laptop dataset includes 121 categories, and we can see that while multi-view prompting provides more possibilities and choices, it still confuses similar aspect categories, i.e., display general and display quality.

heading: 5 Related Works
text: Aspect-base Sentiment Analysis. ABSA has received wide attention in recent years. Early studies focused on extracting or predicting a single sentiment element like aspect term extraction (Qiu et al., 2011; Liu et al., 2015; Ma et al., 2019), aspect cat-
egory detection (Zhou et al., 2015; Bu et al., 2021) or sentiment polarity classification for a given aspect (Wang et al., 2016; Chen et al., 2017; Lei et al., 2018, 2019). Some works further consider the joint prediction of two associated elements (Cai et al., 2020b), including aspect-opinion pair extraction (Wang et al., 2017; Chen et al., 2020), aspect term-polarity co-extraction (Huang and Carley, 2018; Luo et al., 2019; Chen and Qian, 2020). And recent works propose more challenging ABSA tasks to predict sentiment triplets or quadruplets (Chen et al., 2022), the most influential of which are ASTE (Peng et al., 2020; Zhai et al., 2022), TASD (Wan et al., 2020), ASQP (Zhang et al., 2021a) and ACOS with an emphasis on the implicit aspects or opinions (Cai et al., 2020a).
Generative ABSA. Instead of separate or pipeline methods (Phan and Ogunbona, 2020), most recent works attempt to tackle various ABSA problems using a unified framework (Sun et al., 2022). Generative methods achieve good performance in ABSA by mitigating the potential error propagation in pipeline methods and fully exploiting the rich label semantic information (Paolini et al., 2021; Zhang et al., 2022; Yu et al., 2023). They use sentiment element sequence (Zhang et al., 2021d), natural language (Liu et al., 2021a; Zhang
et al., 2021b) and structured extraction schema (Lu et al., 2022b) etc. as the generative targets. Recently proposed LEGO-ABSA (Gao et al., 2022) and UnifiedABSA (Wang et al., 2022c) focus on multi-tasking with task prompts or instruction design. Hu et al. (2022) firstly investigate element ordering and propose methods to augment targetside data with selected orders for the ASQP task. Despite the promising results, the augmentation may confuse the model with multiple targets for the same input (i.e., one-to-many), thus leading to discrepancies between inference and training. We fill the gap and eliminate such confusion by aligning training and inference with multi-view prompt learning.

heading: 6 Conclusion
text: In this work, we introduce an element order-based prompt learning method - MVP, which improves aspect-level opinion information prediction by simple yet effective multi-view results aggregation. Leveraging the intuition of solving problems from different views, MVP advances the research of generative modeling for tuple structure prediction. By combining and permuting the sentiment elements, our multi-tasking model substantially outperforms task-specific models on a variety of ABSA tasks. Detailed experiments show that our method significantly advances the state-of-the-art on benchmark datasets, in both supervised and low-resource settings. We hope our research will shed light on generative tuple prediction.
Limitations
Despite the state-of-the-art performances, our proposed methods still have some limitations for future directions. Firstly, multi-view prompting creates overheads of training and inference proportional to the number of views. For efficiency in practice, according to Figure 3, MVP with a relatively small number of views behaves decently (e.g., 5 or 7). Secondly, we apply a simple yet effective aggregation strategy to combine the results of multiple views. More advanced strategies can be explored. Lastly, experiments only verified the consistent improvement on ABSA tasks, while intuitively, the idea of MVP that leverages multiple views can be expanded to any structure prediction tasks, such as information extraction, emotion-cause pair extraction, and stance detection.
Ethics Statement
We conduct all the experiments on existing datasets widely used in previous public scientific papers. We keep fair and honest in our analysis of experimental results, and our work does not harm anyone. We open-sourced our code for further explorations.
As for the broader impact, this work may foster further research in sentiment analysis using generative methods, contributing to the simplification and automation of user opinion mining in reality. Nevertheless, this work fine-tunes large pre-trained language models to generate sentiment tuples. Due to the large pre-training corpus based on the Internet, the predicted sentiment polarity is subject to unexpected bias with respect to gender, race, and intersectional identities (Tan and Celis, 2019), which needs to be considered more broadly in the field of natural language processing.

heading: Acknowledgements
text: We express our gratitude to Jiayi Li and Chengze Yu for their detailed feedback on a draft of the paper. We also thank Junjie Wang and Taiqiang Wu for their helpful discussion on our presentation. This work was partly supported by the National Key Research and Development Program of China (No. 2020YFB1708200), the "Graph Neural Network Project" of Ping An Technology (Shenzhen) Co., Ltd. and AMiner.Shenzhen SciBrain fund.

heading: A Data Statistics
text: Table 9 shows the data statistics of all datasets of the ASQP, ACOS, ASTE and TASD task. For fair comparison, we keep the same train/dev/test division as previous works.

heading: B Constrained Decoding
text: To make sure the predicted output complies with the mandatory format, we apply the constrained decoding (CD) algorithm in experiments. Rather than search the whole vocabulary for the next token to decode, which may make the model generate invalid sequences that do not match our expectations, CD adjusts the candidate list dynamically in terms of the current state token by token. If the current token is decoded as ’[’, which means the next token should be selected from a list of terms, i.e., [A], [O], [S] and [C]. Additionally, CD tracks the current term and decodes the next following tokens based on Table 5.

heading: C Detailed Experimental Settings
text: Hyper-parameters for all experiments can be found in Table 6. We employ the AdamW (Loshchilov and Hutter, 2019) as the optimizer. All experiments are carried out with an Nvidia RTX 3090 GPU.

heading: D Comparison with ChatGPT
text: D.1 Experiments
We refined the prompt design 4 and evaluated ChatGPT (gpt-3.5-turbo) on four ABSA tasks. Due to budget constraints, we tested it with 200 random samples for each task.
The experimental results, shown in Table 7, highlight the remarkable performance advantage of cross-task transferred and fully supervised MvP compared to the few-shot prompted ChatGPT (+7.06% and +22.84% absolute F1 scores).
4Designed based on https://github.com/RidongHan/ Evaluation-of-ChatGPT-on-Information-Extraction.
D.2 Prompts for ChatGPT We present zero- and few-shot prompts for ASQP (R15) in Listing 1 and 2. For prompts related to other tasks, please refer to our released code.

heading: E Additional Comparison with Single-view Prompting
text: Previously, we conducted ablation studies with a single view on several representative tasks and datasets in both full-data and low-resource settings (see §4.3). Figure 3 illustrates that incorporating multiple views leads to significant improvements, especially in low-resource settings. Additionally, Table 4 showcases the performance degradation resulting from the use of two single-view aggregation strategies.
Here, we present additional comparisons with single-view prompting (designated as SvP) on all tasks, i.e., selecting the best single view for training and inference. We experiment using three singleview selection strategies: 1) random; 2) heuristic: In our pre-experiments, we find that elements ranked ahead of the top selected orders are mostly
free-form terms ’[A]’ and ’[O]’, which have higher uncertainty than ’[C]’ and ’[S]’. Therefore, we propose using the "[A][O][C][S]" order heuristically; 3) rank: choosing a view for each dataset based on the score described in §2.3.1.
As depicted in Table 8, the results show that the SvP methods, whether employing a ranking strategy or a heuristic order chosen through preexperimentation, exhibit limited improvement over a random order (+0.37 and +0.67). By permuting and aggregating results from multiple views, MvP significantly outperforms SvP across all tasks (+1.90). These consistent improvements further elucidate the efficacy of MvP.
Listing 1: Zero-shot Prompt for ASQP (R15). According to the following sentiment elements definition:
− The 'aspect term' refers to a specific feature, attribute, or aspect of a product or service that a user may express an opinion about, the aspect term might be 'null' for implicit aspect. − The 'opinion term' refers to the sentiment or attitude expressed by a user towards a particular aspect or feature of a product or service, the aspect term might be 'null' for implicit opinion. − The 'aspect category' refers to the category that aspect belongs to, and the available catgories includes: 'location general', ' food prices', 'food quality', 'food general', 'ambience general', 'service general', 'restaurant prices', 'drinks prices', ' restaurant miscellaneous', 'drinks quality', 'drinks style_options', 'restaurant general' and 'food style_options'. − The 'sentiment polarity' refers to the degree of positivity, negativity or neutrality expressed in the opinion towards a particular aspect or feature of a product or service, and the available polarities inlcudes: 'positive', 'negative' and 'neutral'.
Recognize all sentiment elements with their corresponding aspect terms, aspect categories, opinion terms and sentiment polarity in the following text with the format of [('aspect term', 'opinion term', 'aspect category', 'sentiment polarity'), ...]:
Listing 2: Few-shot Prompt (10 shots) for ASQP (R15). According to the following sentiment elements definition:
− The 'aspect term' refers to a specific feature, attribute, or aspect of a product or service that a user may express an opinion about, the aspect term might be 'null' for implicit aspect. − The 'opinion term' refers to the sentiment or attitude expressed by a user towards a particular aspect or feature of a product or service, the aspect term might be 'null' for implicit opinion. − The 'aspect category' refers to the category that aspect belongs to, and the available catgories includes: 'location general', ' food prices', 'food quality', 'food general', 'ambience general', 'service general', 'restaurant prices', 'drinks prices', ' restaurant miscellaneous', 'drinks quality', 'drinks style_options', 'restaurant general' and 'food style_options'. − The 'sentiment polarity' refers to the degree of positivity, negativity or neutrality expressed in the opinion towards a particular aspect or feature of a product or service, and the available polarities inlcudes: 'positive', 'negative' and 'neutral'.
Recognize all sentiment elements with their corresponding aspect terms, aspect categories, opinion terms and sentiment polarity in the following text with the format of [('aspect term', 'opinion term', 'aspect category', 'sentiment polarity'), ...]:
Text: never again ! Sentiment Elements: [('null', 'never', 'restaurant general', 'bad')]
Text: the food was mediocre at best but it was the horrible service that made me vow never to go back . Sentiment Elements: [('food', 'mediocre', 'food quality', 'bad'), ('service', 'horrible', 'service general', 'bad')]
Text: we had the lobster sandwich and it was fantastic . Sentiment Elements: [('lobster sandwich', 'fantastic', 'food quality', 'great')]
Text: they have it all −− great price , food , and service . Sentiment Elements: [('null', 'great', 'restaurant prices', 'great'), ('food', 'great', 'food quality', 'great'), ('service', 'great', 'service
general', 'great')]
Text: they even scoop it out nice ( for those on a diet ) not too much not to little . Sentiment Elements: [('null', 'nice', 'food style_options', 'great')]
Text: also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place ! Sentiment Elements: [('place', 'romantic', 'ambience general', 'great'), ('place', 'comfortable', 'ambience general', 'great'), (' service', 'perfect', 'service general', 'great')]
Text: my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian . Sentiment Elements: [('null', 'pleasantly surprised', 'restaurant miscellaneous', 'great')]
Text: i had their eggs benedict for brunch , which were the worst in my entire life , i tried removing the hollondaise sauce completely that was how failed it was . Sentiment Elements: [('eggs benedict', 'worst', 'food quality', 'bad')]
Text: the food is authentic italian − delicious ! Sentiment Elements: [('food', 'authentic italian', 'food quality', 'great'), ('food', 'delicious', 'food quality', 'great')]
Text: a little pricey but it really hits the spot on a sunday morning ! Sentiment Elements: [('null', 'pricey', 'restaurant prices', 'bad'), ('null', 'hits the spot', 'restaurant general', 'great')]
ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: 3 A1. Did you describe the limitations of your work?
7
3 A2. Did you discuss any potential risks of your work? 8
3 A3. Do the abstract and introduction summarize the paper’s main claims? 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? 3
3 B1. Did you cite the creators of artifacts you used? 3, 5
3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? 3, 8

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)? 8

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? 8

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? 1, 5
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. 1, appendix
C 3 Did you run computational experiments? 3

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? appendix
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? 3, appendix

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.)? 3
D 7 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.
D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? No response.
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 crowdworkers explain how the data would be used? No response.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

heading: Future Work
text: Limitations
Despite the state-of-the-art performances, our proposed methods still have some limitations for future directions. Firstly, multi-view prompting creates overheads of training and inference proportional to the number of views. For efficiency in practice, according to Figure 3, MVP with a relatively small number of views behaves decently (e. g. , 5 or 7). Secondly, we apply a simple yet effective aggregation strategy to combine the results of multiple views. More advanced strategies can be explored. Lastly, experiments only verified the consistent improvement on ABSA tasks, while intuitively, the idea of MVP that leverages multiple views can be expanded to any structure prediction tasks, such as information extraction, emotion-cause pair extraction, and stance detection