Venue: NAACL 2024

# InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

 Kevin Scaria\({}^{\dagger}\)   Himanshu Gupta\({}^{\dagger}\)  Siddharth Goyal

Saurabh Arjun Sawant    Swaroop Mishra\({}^{\diamond}\)   Chitta Baral

Arizona State University

{kscaria, hgupta35}@asu.edu

###### Abstract

We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tune the model (T\(k\)-Instruct) for ABSA subtasks, yielding significant performance improvements. Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on Term Extraction (ATE), Sentiment Classification(ATSC) and Sentiment Pair Extraction (ASPE) subtasks. In particular, InstructABSA outperforms the previous state-of-the-art (SOTA) on the Rest14 ATE subtask by 5.69% points, the Rest15 ATSC subtask by 9.59% points, and the Lapt14 AOPE subtask by 3.37% points, surpassing 7x larger models. We get competitive results on AOOE, AOPE, AOSTE, and ACOSQE subtasks indicating strong generalization ability to all subtasks. Exploring sample efficiency reveals that just 50% train data is required to get competitive results with other instruction tuning approaches. Lastly, we assess the quality of instructions and observe that InstructABSA's performance experiences a decline of \(\sim 10\%\) when adding misleading examples 1.

Footnote 1: Experiments and results are available at [https://github.com/kevinscaria/InstructABSA](https://github.com/kevinscaria/InstructABSA)

+ Currently in Amazon (The work was done prior to joining Amazon)

\(\diamondsuit\) Currently in Google Deepmind

## 1 Introduction

Aspect Based Sentiment Analysis (ABSA) plays a vital role in understanding the fine-grained sentiments expressed by users Zhang and Liu (2012). As illustrated in Figure 1, ABSA extracts aspects and classifies the aspect's sentiment polarity by extracting and understanding the author's opinions. Instruction learning paradigm Mishra et al. (2022b); Wei et al. (2022); Gupta et al. (2023) has significantly improved the reasoning abilities of large language models (LLMs) and has shown impressive results across various tasks Wang et al. (2022a); Lu et al. (2022). Owing to its previous success, we propose InstructABSA, instruction learning for aspect based sentiment analysis (ABSA). Our approach involves further instruction tuning of the T\(k\)-Instruct model Wang et al. (2022b) to address six subtasks of ABSA as shown in Fig. 1. We add instruction prompts specific to the downstream ABSA subtasks in the form of task definitions, followed by positive, negative, and neutral examples.

We carried out extensive experiments on the SemEval 2014, 15, and 16 datasets Pontiki et al. (2014, 2015, 2016), and the dataset by Peng et al. (2020) for the AOSTE subask, which comprises the laptops and restaurants domain. Across the

Figure 1: Illustration of the six ABSA subtasks where \(S_{i}\) is the \(i^{th}\) sentence, \(a^{i}\) are the aspect terms, \(sp^{i}\) are the sentiment polarities and \(o^{i}\) is the opinion terms.



subtasks in both domains, InstructABSA outperforms SOTA approaches. Specifically, for the 2014 ATE subtask, we obtain F1-score of 92.3 and 92.76 (Lapt14, Rest14), surpassing SOTA by \(4.37\%\) and \(5.69\%\) points respectively. For the ATCC subtask, InstructABSA attains an accuracy of 84.50 in the Rest15 dataset exceeding the previous results by \(9.59\%\) points. In the Rest14 dataset ATSC subtask, our approach gets a competitive accuracy score of 86.25 compared to the SOTA of 90.86. For the ASPE subtask, InstructABSA achieves F1-score of 79.34 and 79.47 (Lapt14, Rest14), outperforming SOTA by \(3.37\%\) and \(1.4\%\) points, respectively. We get competitive results on AOOE and AOSTE approaches as well (SS4).

We conduct a thorough analysis along several lines of enquiry. We showcase sample efficiency of InstructABSA by achieving competitive scores using roughly 20% of training samples as compared to Varia et al. (2023)'s instruction tuning approach. We compare InstructABSA with fine-tuning methods such as Low-Rank Adaptation (LoRA) Hu et al. (2021) to find that there is a sizebale gap of \(\sim 20\%\). To understand the effect of different instructions for ABSA, we change the prompts on the lines of definition and task manipulation. We find that delusive examples roughly decrease the approaches results by \(\sim 10\%\) giving a strong evidence of the impact of instructions on InstructABSA. We also provide evidence of cross-domain and joint-domain generalizations arising as part of our proposed approach.

**Contributions:**(a) we introduce InstructABSA, which achieves performance gains on ABSA subtasks of SemEval 2014,15 and 16 datasets, surpassing the previous SOTA models. (b) Despite using a 200M model, InstructABSA outperforms or get competitive results over the prior SOTA models with 1.5B parameters. (c) Finally, we provide an analysis of the impact of our method in terms of sample efficiency, adapter methods, effect of instruction and domain generalization.

## 2 InstructABSA: Instruction Learning for ABSA

We describe the mathematical formulation of ABSA subtasks and the proposed approach. Let \(S_{i}\) represent the \(i^{th}\) review sentence in the training sample, where:\(S_{i}=w_{i}^{1},w_{i}^{2},...,w_{i}^{n}\) with \(n\) as the number of tokens in the sentence. Each \(S_{i}\) contains a set of aspect terms denoted by \(A_{i}=a_{i}^{1},a_{i}^{2},...,a_{i}^{m}|m\leq n\) and the corresponding opinion terms, aspect category and sentiment polarities for each aspect term are denoted by \(O_{i}=o_{i}^{1},o_{i}^{2},...,o_{i}^{m}~{}C_{i}=c_{i}^{1},c_{i}^{2},...,c_{i} ^{m}\) and \(SP_{i}=sp_{i}^{1},sp_{i}^{2},..,sp_{i}^{m}\) respectively, where \(sp_{i}^{k}\in[positive,negative,neutral]\) The ABSA tasks are described as follows:

\(ATE:A_{i}=LM_{ATE}(S_{i})\)

\(ATSC:sp_{k}^{k}=LM_{ATCSC}(S_{i},a_{i}^{k})\)

\(ASPE:[A_{i},SP_{i}]=LM_{ASPE}(S_{i})\)

\(AOOE:o_{i}^{k}=LM_{AOOE}(S_{i},a_{i}^{k})\)

\(AOPE:[A_{i},O_{i}]=LM_{AOPE}(S_{i})\)

\(AOSTE:[A_{i},O_{i},SP_{i}]=LM_{AOSTE}(S_{i})\)

\(ACOSQE:[A_{i},C_{i},O_{i},SP_{i}]=LM_{ACOSQE}(S_{i})\)

In these equations, \(LM\) represents the language model, and the corresponding inputs and outputs are defined accordingly. As part of our approach, we instruction tune \(LM_{subtask}\) by prepending task-specific instruction prompts \(Inst\) to each input sample to arrive at \(LM_{subtask}^{Inst}\). Here, \(Inst=Definition+2\times PositiveExample+2\times NegativeExample+2 \times NeutralExample\) For the \(LM\), we use "*tk-instruct-base"" as the model. The \(definition\) involves the task definition for each subtask. Contrary to the standard instruction tuning prompts proposed by Wang et al. (2022), \(PositiveExample\) and \(NegativeExample\) here represent examples that have a positive and negative sentiment example respectively. Additionally, we introduce \(NeutralExample\) which is an example that has neutral sentiment respectively(SSF).

## 3 Experimental Setup

We use the T\(k\)-Instruct-base-def-pos 2 as the instruction-tuned model \(LM_{Inst}\). We use two configurations of instructions as prompts for our experiments. InstructABSA-1 has the instruction prompt that includes the definition of the ABSA subtasks followed by 2 positive examples for the respective task. InstructABSA-2 has the definition followed by 2 positive, negative, and neutral examples.

Footnote 2: [https://huggingface.co/allenai/](https://huggingface.co/allenai/)

tk-instruct-base-def-pos 

HyperparametersGPU: 1xNvidia Tesla P40, Train Batch Size: 16 for ATE and ATSC, 8 for other subtasks. Gradient Accumulation Steps: 2, Initial learning rate: 5e-5, Num of Epochs: 4

Evaluation MetricFollowing previous approaches Zhang et al. (2021); Luo et al. (2020), we use the micro F1-score for ATE, AOPE, AOOE, AOPE, AOSTE, and the accuracy for ATSC.

## 4 Results and Analysis

### Sub Task Results

Tables 1 - 7 denotes the results of ATE, ATSC, ASPE, AOOE, AOPE, AOSTE and ACOSQE subtasks respectively. All the results reported are the average values from 5 runs for each experiment. For **ATE** subtask (Table 1), InstructABSA surpasses SOTA on Lapt14, Rest14, 15, and 16 datasets surpassing 7x larger models Hosseini-Asl et al. (2022) uses GPT-2 with 1.5B parameters). For **ATSC** subtask, InstructABSA-2 achieves SOTA of Rest 15 while remaining competitive of Lapt and Rest 14 dataset. For the **ASPE subtask** (Table 3), InstructABSA acheives SOTA for all four datasets. In the **AOOE** subtask (Table 4) InstructABSA achieves an F1 score of 76.42 and 77.16 for the Lapt14 dataset, outperforming IOG and ONG.

In the **AOPE** subtask (Table 5), InstructABSA suffers compared to the existing models. For the **AOTE** subtask (Table 6), Seq2Path achieves the highest F1 scores for the datasets, however, our models achieve competitive results for Rest14. Finally, for the **ACOSQE** subtask, InstructABSA performs \(\sim 1.1\%\) points more than the previous best. The performance of InstructABSA in AOPE, AOSTE, and ACOSQE is subpar as compared to ATE and ATSC due to exposure bias. For sentiment pair extraction tasks, the model had to decode only the aspect terms followed by sentiments that were constrained to positive, negative, and neutral labels. However, for the opinion pair extraction tasks and triplet extraction tasks, the model suffers higher exposure bias since the opinion terms are not grounded and could potentially be any word in the vocabulary Zhang et al. (2020).

\begin{table}
\begin{tabular}{l r r r r} \hline \hline

- \\ GRACE & 87.93 & 85.45 &
- \\ BARTABSA & 83.52 & 87.07 & 75.48 &
- \\ IT-MTL & 76.93 &
- & 74.03 & 79.41 \\ \hline InstructABSA1 & 91.40 & **92.76** & 75.23 & **81.48** \\ InstructABSA2 & **92.30** & 92.10 & **76.64** & 80.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ATE subtask results denoting F1 scores. GPT2\(med\), GRACE, BARTABSA and IT-MTL results are from Hosseini-Asl et al. (2022), Luo et al. (2020), Yan et al. (2021) and Varia et al. (2023) respectively.

\begin{table}
\begin{tabular}{l r r r r} \hline \hline

- \\ BARTABSA & 67.37 & 73.56 & 66.61 &
- \\ IT-MTL & 66.07 &
- & 67.06 & 74.07 \\ \hline InstructABSA1 & 78.89 & 76.16 & 69.02 & **74.24** \\ InstructABSA2 & **79.34** & **79.47** & **69.39** & 73.06 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ASPE subtask results denoting F1 scores. GRACE, BARTABSA and IT-MTL results are from Luo et al. (2020), Yan et al. (2021) and Varia et al. (2023).

\begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Model** & **Lept14** & **Rest14** & **Rest15** & **Rest16** \\ \hline HGAE & 70.99 & 80.23 & 71.91 & 81.60 \\ ONG & 76.77 & 82.33 & 78.81 & 86.01 \\ BARTABSA & **80.55** & **85.38** & 80.52 & **87.92** \\ \hline InstructABSA1 & 76.42 & 80.78 & 80.41 & 83.07 \\ InstructABSA2 & 77.16 & 81.08 & **81.34** & 83.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AAOOE subtask results denoting F1 scores. IOG, ONG and BARTABASA are from Fan et al. (2019), Pouran Ben Veyseh et al. (2020) and Yan et al. (2021) respectively.

\begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Model** & **Lept14** & **Rest14** & **Rest15** & **Rest16** \\ \hline Seq2Path & **74.29** & **77.35** & **71.84** & **79.09** \\ GAS & 69.55 & 75.15 & 67.93 & 75.42 \\ BMRC & 67.45 & 76.23 & 68.60 & 76.52 \\ \hline InstructABSA1 & 60.75 & 70.46 & 60.31 & 72.04 \\ InstructABSA2 & 61.74 & 71.37 & 62.59 & 70.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the AOPE subask denoting F1 scores. Seq2Path, GAS and BMRC are from Mao et al. (2022), Zhang et al. (2021) and Chen et al. (2021) respectively.

 



### Analysis

In this subsection, we analyze InstructABSA on multiple line of enquiries.

Cross-Domain and Joint Domain Evaluation:In cross domain setting, we train the model on a train set from one domain and test on test set from another domain. In joint domain setting, the train data of the domains (laptops and restaurants) are combined to train the model, and it is evaluated on both test sets. Both experiments are performed on ATE, ATSC and ASPE subtasks for both instruction-tuned models (InstrtABSA-1 & 2). Table 8 presents the cross domain experiment results. When trained on Laptl4 and tested on Rest14, InstructABSA-1 shows a drop in F1-score for the ATE and Joint Task compared to InstructABSA-2. For the ATSC task, similar trends were obtained with an accuracy of 75.53 from InstructABSA-1 and 80.56 from InstructABSA-2. The joint domain experiments are present in Table 9. The availability of additional training data for ATE subtask helps the language models as the proposed model surpasses the previously achieved SOTA. We also analyzed the performance of InstructABSA in a multi-task learning setup and find that our model achieves comparable results as presented in table 11.

Delusive examples reduce InstructABSA's performanceWe analyze the impact of instruction tuning along the lines of experiments proposed by kung2023learning, focusing on task definition and example manipulation. In task definition manipulation, we explore original, simplified, and empty definitions, but only use the empty configuration with vanilla T5 and T\(k\)-instruct models. In task example manipulation, we study original, delusive, and empty examples, as well as additional configurations. Detailed results can be found in Figure 4 and Tables 15, 16, and 17. Notably, InstructABSA-1 and 2 outperform the vanilla models, highlighting the effectiveness of instruction tuning for most

\begin{table}
\begin{tabular}{l r r r r} \hline \hline
* & 43.84 & 52.94 & 53.75 \\ \hline InstructABSA1 & 60.67 & 70.50 & 60.63 & 68.15 \\ InstructABSA2 & 61.86 & 71.17 & 59.98 & 70.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the AOSTE subash denoting F1 scores. Seq2Path, IT-MTL and BMRRC are from Mao et al. (2022), Chen et al. (2021), and Varia et al. (2023).

\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Model** & **ATE** & **ATSC** & **ASPE** \\ \hline  & Laptl4 & Rest14 & Laptl4 & Rest14 & Leptl4 & Rest14 \\ \hline LoLRA A & 73.51 & 79.43 & 55.79 & 59.08 & 53.19 & 57.28 \\ LaR1 & 73.57 & 78.32 & 54.30 & 59.16 & 52.30 & 57.19 \\ LaRAA & 75.52 & 78.74 & 54.94 & 59.58 & 54.43 & 56.98 \\ InstructABSA-1 & 71.61 & 76.93 & 58.57 & 58.64 & 55.87 & 58.64 \\ InstrtARSA-1 & 91.40 & 92.76 & 50.62 & 86.25 & 78.39 & 76.16 \\ InstrtARSA-2 & 92.30 & 92.10 & 51.56 & 85.17 & 79.34 & 79.47 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of LoRA PEFT and InstructABSA-1 and InstructABSA-2 across all subtasks. 8, 16, 32 and 64 in LoRA denote the rank of the adapter method.

\begin{table}
\begin{tabular}{l l r|r r} \hline \hline
**Task** & **Model** & **ATE** & **ATSC** & **ASPE** \\ \hline  & Laptl4 & Rest14 & Laptl4 & Rest14 & Leptl4 & Rest14 \\ \hline  & 73.51 & 79.43 & 55.79 & 59.08 & 53.19 & 57.28 \\ LaRIA 16 & 73.57 & 78.32 & 54.30 & 59.16 & 52.30 & 57.19 \\ LaRLA 32 & 75.52 & 78.74 & 54.94 & 59.58 & 54.43 & 56.98 \\ InstructABSA-1 & 71.61 & 76.93 & 58.57 & 58.64 & 55.87 & 58.64 \\ InstrtARSA-1 & 91.40 & 92.76 & 50.62 & 86.25 & 78.39 & 76.16 \\ InstrtARSA-2 & 92.30 & 92.10 & 51.56 & 85.17 & 79.34 & 79.47 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of Multi-task learning evaluation on the 4 datasets.

\begin{table}
\begin{tabular}{l r r|r r r} \hline \hline
**Dataset** & **ATE** & **STSC** & **ASPE** & **AOOE** & **AOSTE** & **ACOSQE** \\ \hline Laptl4 & 93.41 & 82.33 & 80.89 & 79.12 & 62.94 & 62.31 & 62.43 \\ ResNet14 & 94.16 & 87.13 & 81.06 & 82.87 & 71.89 & 72.35 & 61.16 \\ Rest15 & 78.53 & 86.67 & 71.31 & 82.78 & 64.52 & 61.13 & 64.23 \\ ResIt6 & 81.98 & 91.02 & 74.98 & 84.56 & 72.38 & 72.34 & 66.21 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of LoRA PEFT and InstructABSA-1 and InstructABSA-2 across all subtasks. 8, 16, 32 and 64 in LoRA denote the rank of the adapter method.

\begin{table}
\begin{tabular}{l r r|r r} \hline \hline
**Train** & **Test** & **Model** & **ATE** & **ATSC** & **ASPE** \\ \hline \multirow{2}{*}{Rest14} & \multirow{2}{*}{Laptl4} & InstructABSA-1 & 71.98 & 80.56 & 64.30 \\  & & InstructABSA-2 & 71.83 & 82.44 & 65.30 \\ \hline \multirow{2}{*}{Laptl4} & \multirow{2}{*}{Rest14} & InstructABSA-1 & 62.85 & 75.53 & 55.06 \\  & & InstructABSA-2 & 76.85 & 80.56 & 62.95 \\ \hline \multirow{2}{*}{Rest15} & \multirow{2}{*}{Hotel15} & InstructABSA-1 & 74.51 & 87.65 & 66.88 \\  & & InstructABSA-2 & 70.53 & 89.74 & 67.82 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the cross-domain evaluation where the model is trained on Laptl4 and the test set is of Rest14 and vice versa. The results of the model trained on Rest15 and evaluated on Hotel15 is also reported.



ABSA subtasks.

**Competitive scores with just 50% train samples** Gupta et al. (2023) showcased the effects of sample efficiency via instruction tuning. Following that work, we explore the performance of instruction tuning by using a smaller percentage of the training set. We carry out experiments to identify the sample efficiency gains for ABSA subtasks. The results are presented in Figure 3 and Table 18. We get competitive scores with our best scores when using roughly 50% train samples, demonstrating sample efficiency of InstructABSA. Figure 3 also showcases the performance of the vanilla T5 base model finetuned with the same number of samples. As shown in the figure, the vanilla model's performance is consistently lower compared to InstructABSA.

Hard Case Analysis:We analyze the performance of instruction tuning on hard samples (HDS), viz. samples that have more than one aspect with a different sentiment polarity. From table 12 it can be seen that InstructABSA achieves competitive performance in hard cases.

Adapter methods leading to poor performanceWe compare the performance of parameter efficient finetuning method Low-Rank Adaptation (LoRA)Hu et al. (2021) with our instruction tuning approach InstructABSA. LoRA can lead to significant improvements in memory efficiency and computational efficiency, but it can also lead to a drop in performance. The experiment is performed on all the subtasks, and the results are presented in Table 10. As seen in the table a drop of 13.32% points in ATE, 26.8% points in ATSC and 19.8% points in ASPE. The drop in scores is significant to overlook when aiming to reap the advantages of a computationally optimized finetuning method.

## 5 Conclusion

We proposed InstructABSA, an instruction-tuned modeling approach for all subtasks of ABSA. Our findings show that InstructABSA surpassed the previous scores on several tasks and achieved competitive scores on the rest using a significantly smaller model than previous approaches. We further analyzed the performance of the approach along several lines of enquiry revealing several interesting findings.

### Limitations

Our study is limited to the Sem Eval 2014, 15, and 16 datasets, that are widely used in recent works. Future studies should include the exten

\begin{table}
\begin{tabular}{l|r r r r} \hline \hline
**Dataset** & **AGDT** & **GCAE** & **IABSA1** & **IABSA2** \\ \hline
**Rest14** & 51.3 & 56.73 & 56.21 & 57.13 \\
**Lapt14** & 60.33 & 47.06 & 52.36 & 53.01 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of the hard case analysis. AGDT and GCAE are from Liang et al. (2019) and Xue and Li (2018) respectively.

Figure 3: Comparison of sample efficiency on ATE, AOSTE and ATSC subtasks between InstructABSA-2 and vanilla model. Sample size is % of training data.

Figure 2: Comparison of various instruction configuration and its performance on ATE, AOSTE and ATSC subtasks. vanilla_t5 and instruct_t5 represent the base T5 model with and without instruction tuning on the dataset. absa1 includes a definition followed by 2 positive exemplars, absa2 includes a definition followed by 2 positive, negative, and neutral examples, and finally, absa5 is the delusive configuration with incorrect input and output mappings respectively.

 

sion of this work on other ABSA datasets to test the generalizability of our findings. We conducted our experiments using a 200M model, which may limit the applicability of our findings to smaller models. Future studies could consider using even smaller instruction-tuned models to analyze their performance. Our study was conducted using T\(k\)-Instruct models for the English language. As a result, our findings may not be directly applicable to other languages. Future studies should include a multilingual dataset and a multilingual instruction-tuned model to investigate the model's performance across different languages.

## Ethical Considerations

We acknowledge that the T5 model used in our experiments may have inherent biases due to the pretraining and instruction-tuning data used. While stress testing was not conducted, we believe that from our research no additional issues arise related to privacy, fairness, bias, and discrimination. We Our work directly contributes to the topic of aspect based sentiment analysis and we believe that our work will have a positive impact on the scientific community. We remain dedicated to advancing the responsible use of AI and will continue to prioritize ethical considerations in all our future research endeavors.

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## Appendix A Choosing Samples as Instruction Exemplars:

From Table 14, it can be noticed that the distribution of count of aspects across Lapt14, Rest14, Rest15, and Rest16 datasets is centered around one, two, and three aspects which account for 30%, 11%, and 4.5% of total aspects. Thus for our instruction exemplars, we randomly select samples that have aspects ranging between 1 and 3. We exclude these exemplars during evaluation.

## Appendix B Instruction Effectiveness Study

To validate the effect of instruction tuning on the performance of various ABSA sub tasks, We analyse effect of instruction tuning along the lines of experiments proposed by Kung and Peng (2023). We carry out our analysis on two aspects: task definition manipulation and task example manipulation. In _task definition manipulation_, controlled experiments are conducted to examine whether models truly comprehend and utilize the semantic meaning of task definitions. Three levels of granularity was proposed viz. _original_, _simplified_, and _empty_. The simplified version removes all semantic components from the task definition, leaving only the output space information. The empty version eliminates the task definition altogether. However, as part of the task definition manipulation experiment we only conduct the empty configuration with vanilla_t5 and vanilla_tk where t5 is the T5-base model and tk is the T\(k\)-instruct base model. In _task example manipulation_, the influence of task examples on model learning is investigated. Three types of task examples are compared: _original_, _delusive_, and _empty_. The original setup includes one/two positive example (absa1), while the delusive examples consist of negative examples with incorrect input-output mappings (absa6). The empty setup excludes task examples during training (task_def_only). We additionally carry out different configuration of task examples and call it additions, where we add 2 positive, negative and neutral examples (absa2), 2 negative (absa3), 2 neutral (absa4) and 1 positive, negative and neutral example (absa5). The detailed reports are presented in the Figure 4 and Tables 15, 16 and 17 . It is evident that for most ABSA subtasks, the instruction configuration of InstructABSA-1 and 2 yields the best performance. Additionally, it can be seen that both the vanilla models do not give the best results solidifying the effectiveness of further instruction tuning.

## Appendix C Detailed Dataset Description:

Table 14 displays the dataset description with respect to the count of aspect terms for all subtasks. For the training set, 1557 reviews in Lapt14 and 1020 reviews in Rest14 have no aspect terms and their corresponding polarities. Similarly, in the test set, 378 reviews in Lapt14 and 194 reviews in the Rest14 have no aspect terms and corresponding polarities. The dataset description for the ATSC subtask is presented in Table 13. To maintain consistency with the previous approaches for the ATSC task, we also ignore conflict labels.



(2022) showed that adding knowledge with instruction helps LMs understand the context better. Furthermore, several approaches have been proposed to improve model performance using instructions, including (Wang et al., 2022; Luo et al., 2022; Mishra and Nouri, 2022) Several studies are present that show adding knowledge with instruction helps LMs understand the context better (Gupta et al., 2021).

## Appendix E Additional Tables for Plots

The following section presents the absolute non aggregated numbers for the plots generated to analyse the instruction effectiveness (Figure 4) as well as the sample efficiency plots (Figure 3). The following analysis was conducted on the 3 subtasks viz. ATE, ATSC and AOSTE. This was based on the level of difficulty of the tasks. To balance out the analysis across tasks of various difficulties, we chose the easiest task which is just task extraction. It was followed by ATSC task which is more complicated since the model has to learn associations of the aspect term and its corresponding sentiment polarity. Finally the task with maximum difficulty was triplet extraction since the model has to extract all triplets given a sentence.

Table 15 presents the performance metrics in terms of F1 score for the ATE subtask for the 4 datasets when instruction tuned with various configuration of instructions as mentioned in SS4.2. Similarly Table 16 presents the F1 scores for the ATSC subtask when instruction tuned with various configuration of instructions as mentioned in SS4.2. Table 17 presents the F1 scores for the AOSTE subtask

\begin{table}
\begin{tabular}{l|c|c|c|c|c|c|c|c|c|c|c|c} \hline Dataset & Split & \#NO & \#1 & \#2 & \#3 & \#4 & \#5 & \#6 & \#7 & \#8 & \#9 & \#10+ & \#Total \\ \hline Lapt14 & Train & 1557 & 930 & 354 & 140 & 43 & 10 & 6 & 3 & 1 & - & 1 & 3045 \\  & Test & 378 & 266 & 105 & 34 & 10 & 6 & 1 & - & - & - & - & 800 \\ \hline Rest14 & Train & 1020 & 1022 & 572 & 269 & 104 & 30 & 15 & 5 & 3 & 1 & - & 3041 \\  & Test & 194 & 290 & 186 & 80 & 30 & 14 & 3 & 2 & - & - & 1 & 800 \\ \hline Rest15 & Train & 482 & 576 & 174 & 58 & 22 & 2 & - & - & 1 & - & - & 1315 \\  & Test & 284 & 294 & 82 & 18 & 6 & - & 1 & - & - & - & - & 685 \\ \hline Hotels15 & Test & 98 & 135 & 23 & 7 & 2 & 1 & - & - & - & - & - & 266 \\ \hline Rest16 & Train & 766 & 868 & 258 & 76 & 28 & 2 & 1 & - & 1 & - & - & 2000 \\  & Test & 256 & 298 & 87 & 22 & 9 & 3 & - & - & - & - & 1 & 676 \\ \hline \end{tabular}
\end{table}
Table 14: Count of Aspects for the ATE, ASTE, AOOE, AOPE and AOSTE subtasks. \(\#k\) is the count of samples that have \(k\) aspects/aspect-sentiment polarity pairs in them. #NO is the number of samples that have no aspect/aspect-sentiment polarity pairs in them.

Figure 4: Comparison of various instruction configuration and its performance on ATE, AOSTE and ATSC subtasks. Vanilla_t5 and Vanilla_tk represent the models trained without any instruction. absa1, absa2, absa3, absa4, absa5 are different instruction configurations that include a definition followed by 2 positive, 2 positive, negative and neutral examples, 2 negative examples, 2 neutral examples, 1 positive, negative and neutral examples and finally examples with incorrect input and output mappings respectively. task_def_only only contains the task definitions.

 

when instruction tuned with various configuration of instructions as mentioned in SS4.2. Finally, Table 18, describes the values for the sample efficiency plot. This plot presents the raw unaggregated numbers for ATE, ATSC and AOSTE.

## Appendix F InstructABSA prompt examples

The instruction prompts for InstructABSA-1, and InstructABSA-2 are presented in detail for all three ABSA subtasks. Table 19, 20, and 21 presents the prompts provided for InstructABSA-2 model for the ATE, ATSC, and AOPE, respectively.

For the InstructABSA-1 model, the instruction prompts are similar, with the difference that negative and neutral examples are not provided in the instruction prompts.

\begin{table}
\begin{tabular}{c c c c c} \hline Instruction & Lapt14 & Rest14 & Rest15 & Rest16 \\ Type & Lapt14 & Rest14 & Rest15 & Rest16 \\ \hline vanilla_t5 & 59.42 & 80.70 & 72.41 & 81.44 \\ instruct\_t5 & 62.56 & 81.30 & 74.03 & 82.54 \\ vanilla\_tk & 71.98 & 83.10 & 78.91 & 85.86 \\ task\_def\_only & 74.56 & 83.27 & 80.12 & 86.45 \\ absa1 & 79.37 & 85.15 & 82.98 & 89.09 \\ absa2 & 80.84 & 84.47 & 83.37 & 88.66 \\ absa3 & 79.01 & 82.34 & 81.67 & 87.12 \\ absa4 & 77.18 & 80.21 & 79.97 & 85.58 \\ absa5 & 75.35 & 78.08 & 78.27 & 84.04 \\ absa6 & 70.12 & 75.95 & 76.57 & 82.50 \\ \hline \end{tabular}
\end{table}
Table 16: Tabular Results Instruction Effectiveness Plot for ATSC

Figure 5: Formulation of InstructABSA for ATSC task. The input consists of an instruction prompt and a sentence. The output label is the sentiment polarity for the corresponding aspect.

\begin{table}
\begin{tabular}{c c c c c} \hline Instruction & Lapt14 & Rest14 & Rest15 & Rest16 \\ Type & Lapt14 & Rest14 & Rest15 & Rest16 \\ \hline vanilla\_t5 & 53.53 & 66.48 & 64.53 & 52.73 \\ instruct\_t5 & 54.72 & 67.15 & 63.88 & 55.30 \\ vanilla\_tk & 58.29 & 69.16 & 61.93 & 63.01 \\ task\_def\_only & 59.48 & 69.83 & 61.28 & 65.58 \\ absa1 & 60.67 & 70.50 & 60.63 & 68.15 \\ absa2 & 61.86 & 71.17 & 59.98 & 70.72 \\ absa3 & 58.98 & 69.65 & 57.83 & 69.12 \\ absa4 & 56.10 & 68.13 & 55.68 & 67.52 \\ absa5 & 53.22 & 66.61 & 53.53 & 65.92 \\ absa6 & 50.34 & 65.09 & 51.38 & 64.32 \\ \hline  & & & & \\ \end{tabular}
\end{table}
Table 17: Tabular Results Instruction Effectiveness Plot for AOSTE 



\begin{table}
\begin{tabular}{l r r r} \hline Task & \multicolumn{1}{c}{Sample Size} & No Instruction & \multicolumn{1}{r}{InstrtABSA-2} \\ \hline ate & 10 & 49.15 & 71.81 \\ ate & 20 & 56.12 & 74.06 \\ ate & 50 & 68.30 & 82.37 \\ ate & 100 & 73.13 & 92.20 \\ atsc & 10 & 37.24 & 49.67 \\ atsc & 20 & 51.23 & 62.34 \\ atsc & 50 & 63.45 & 73.21 \\ atsc & 100 & 70.06 & 82.65 \\ aoste & 10 & 26.34 & 48.98 \\ aoste & 20 & 45.78 & 59.24 \\ aoste & 50 & 54.29 & 63.25 \\ aoste & 100 & 60.05 & 67.16 \\ \hline \end{tabular}
\end{table}
Table 18: Tabular Results of Sample Efficiency Plots



\begin{table}
\begin{tabular}{l l} \hline
**Task** & Aspect Term Sentiment Classification (ATSC) \\ \hline
**Definition** & The output will be ’positive’, ‘negative’ or ‘neutral’ if the sentiment of the identified aspect in the input is positive, negative or neutral respectively \\  & For the aspects which are classified as noaspectterm, the sentiment is none. \\ \hline
**Positive** & Example Input 1: With the great variety on the menu, I eat here often and never get bored. \\
**Example** & Aspecet: menu \\  & Example Output 1: positive \\  & Example Input 2: Great food, good size menu, great service, and an unpretentious setting. \\  & Example Output 2: positive \\ \hline
**Negative** & Example Input 1: They did not have mayonnaise, forgot our toast, left out ingredients \\
**Example** & (i.e., cheese in an omelet), below hot temperatures and the bacon was \\  & so overcooked it crumbled on the plate when you touched it. Aspect: toast \\  & Example Output 1: negative \\  & Example Input 2: The seats are uncomfortable if you are sitting against the wall \\  & on wooden benches. Aspect: seats \\  & Example Output 2: negative \\ \hline
**Neutral** & Example Input 1: I asked for a seltzer with lime, no ice. Aspect: seltzer with lime \\
**Example** & Example Output 1: neutral \\  & Example Input 2: They wouldn't even let me finish my glass of wine before offering another. \\  & Negative input 2: The seats are uncomfortable if you are sitting against the wall \\  & on wooden benches. \\  & Negative output 2: seats \\ \hline
**Neutral** & Neutral Input 1: I asked for a seltzer with lime, no ice. \\
**Example** & Neutral Output 1: seltzer with lime \\  & Neutral Input 2: They wouldn’t even let me finish my glass of wine before offering another. \\  & Neutral Output 2: glass of wine \\ \hline
**Input** & Now complete the following example- \\  & input: My son and his girlfriend both wanted cheeseburgers and they were huge! \\  & output: cheeseburgers \\ \hline \end{tabular}
\end{table}
Table 19: Illustrating InstructABSA-2 instruction prompting for the ATE sub task.





\begin{table}
\begin{tabular}{l l} \hline
**Task** & Aspect Oriented Opinion Extraction (AOOE) \\ \hline
**Definition** & Definition: The output will be the opinion/describing word of the aspect terms in the sentence. In cases where there are no aspects the output should be none. \\ \hline
**Positive** & Example Input 1: Faan ’s got a great concept but a little rough on the delivery. \\
**Example** & Example Output 1: delivery:rough \\  & Example Input 2: it is of high quality , has a killer GUI , is extremely stable, \\  & is highly expandable. The aspect is GUI. \\  & Example Output 2: killer \\ \hline
**Negative** & Example Input 1: One night I turned the freaking thing off after using it , the next day \\
**Example** & I turn it on , no GUI , screen all dark,.. The aspect is GUI. \\  & Example Output 1: no \\  & Example Input 2: I can barely use any usb devices because they will \\  & not stay connected properly . The aspect is usb devices. \\  & Example Output 2: not stay connected properly \\ \hline
**Neutral** & Example Input 1: However, ..external mouse unnecessary. The aspect is external mouse. \\
**Example** & Example Output 1: unnecessary \\  & Example Input 2: ... extended warranty and they refused. The aspect is extended warranty. \\  & Example Output 2: refused \\ \hline
**Input** & Now complete the following example- \\  & input: My son ... cheeseburgers and they were huge!. The aspect is cheeseburger s. \\  & output: huge \\ \hline \end{tabular}
\end{table}
Table 22: Illustrating InstructABSA-2 instruction prompting for the AOOE subtask.

\begin{table}
\begin{tabular}{p{43.7pt} p{56.9pt}} \hline
**Task** & Aspect Sentiment Pair Extraction (ASPE) \\ \hline
**Definition** & Definition: The output will be the aspects (both implicit and explicit), and the aspects sentiment polarity. In cases where there are no aspects, the output \\  & should be no aspect-tern: none. \\ \hline
**Positive** & Example Input 1: With the great variety on the menu, I eat here often and never get bored. \\
**Example** & Example Output 1: menu:positive \\  & Example Input 2: Great food, good size menu, great service, and an unpretentious setting. \\  & Example Output 2: food:positive \\ \hline
**Negative** & Example Input 1: They did not have mayonnaise, forgot our toast, left out ingredients (i.e., cheese in an omelet), below hot temperatures, and the bacon was so overcooked it crumbled on the plate when you touched it. \\  & Example Output 1: toast:negative \\  & Example Input 2: The seats are uncomfortable if you are sitting against the wall on wooden benches. Aspect: seats \\  & Example Output 2: negative \\ \hline
**Neutral** & Example Input 1: I asked for a seltzer with lime, no ice. \\
**Example** & Example Output 1: seltzer with lime: neutral \\  & Example Input 2: They wouldn’t even let me finish my glass of wine before offering another. \\  & Example Output 2: glass of wine:neutral \\ \hline
**Input** & Now complete the following example- \\  & input: My son and his girlfriend both wanted cheeseburgers and they were huge! \\  & output: cheeseburgers: positive \\ \hline \end{tabular}
\end{table}
Table 21: Illustrating InstructABSA-2 instruction prompting for the ASPE subtask.





\begin{table}
\begin{tabular}{l l} \hline
**Task** & Aspect Opinion Sentiment Triplet Extraction (AOSTE) \\ \hline
**Definition** & Definition: The output will be the aspect terms in the sentence followed by their describing words and sentiment polarity. \\ \hline
**Positive** & Example Input 1: I charge it at night and skip taking the cord with me because of the good battery life. \\  & Example Output 1: battery life:good:positive \\  & Example Input 2: it is of high quality , has a killer GUI , is extremely stable, \\  & is highly expandable,... good applications,.. easy to use. \\  & Example Output 2: quality:high; GUI:killer, applications:good, use:easy \\ \hline
**Negative** & Example Input 1: A month or so ago , the freaking motherboard just died . \\
**Example** & Example Output 1: motherboard:freaking \\  & Example Input 2: I had always used PCs ....crashing and the poorly designed \\  & Operating systems that were never very intuitive \\  & Example Output 2: operating systems:poorly designed, operating systems: never very intuitive \\ \hline
**Neutral** & Example Input 1: It has a 10 hour ... when you ’re doing web browsing and word editing , \\
**Example** & making it perfect for the classroom or office, ... \\  & Example Output 1: web browsing:perfect, word editing:perfect \\  & Example Input 2: no complaints with their desktop , and maybe because it just sits \\  & on your desktop... which could jar the hard drive , or the motherboard \\  & Example Output 2: hard drive:jar, motherboard:jar \\ \hline
**Input** & Now complete the following example- \\  & input: Boot time is super fast , around anywhere from 35 seconds to 1 minute \\  & output: Boot time:superfast \\ \hline \end{tabular}
\end{table}
Table 23: Illustrating InstructABSA-2 instruction prompting for the AOPE subtask.

\begin{table}
\begin{tabular}{p{48.3pt} p{76.9pt}} \hline
**Task** & Aspect Opinion Pair Extraction (AOE) \\ \hline
**Definition** & Definition: The output will be the aspect terms in the sentence followed by its describing/opinion term. \\ \hline
**Positive** & Example Input 1: I charge it at night and skip taking the cord with me because of the good battery life. \\  & Example Output 1: battery life:good \\  & Example Input 2: it is of high quality , has a killer GUI , is extremely stable, \\  & is highly expandable,... good applications,... easy to use. \\  & Example Output 2: quality:high;positive, GUI:kille:positive \\ \hline
**Negative** & Example Input 1: A month or so ago , the freaking motherboard just died . \\
**Example** & Example Output 1: motherboard:freaking \\  & Example Input 2: I had always used PCs .....crashing and the poorly designed \\  & OS that were never very intuitive \\  & Example Output 2: OS:poorly designed:negative, OS: never very intuitive:negative \\ \hline
**Neutral** & Example Input 1: It has a 10 hour ... when you ’re doing web browsing and word editing , \\
**Example** & making it perfect for the classroom or office, ... \\  & Example Output 1: web browsing:perfect:neutral, word editing:perfect:neutral \\  & Example Input 2: no complaints with their desktop , and maybe because it just sits \\  & on your desktop... which could jar the hard drive , or the motherboard \\  & Example Output 2: hard drive:jar:neutral, motherboard:jar:neutral \\ \hline
**Input** & Now complete the following example- \\  & input: Boot time is super fast , around anywhere from 35 seconds to 1 minute \\  & output: Boot time:superfast:positive \\ \hline \end{tabular}
\end{table}
Table 24: Illustrating InstructABSA-2 instruction prompting for the AOPE subtask.





\begin{table}
\begin{tabular}{l l} \hline
* 2 Negative Examples \\ \hline
**Definition** & Definition: The output will be the the aspect terms in the \\  & sentence followed by their describing/opinion term. \\ \hline
**Negative** & Example Input 1: A month or so ago , the freaking motherboard just died . \\
**Example** & Example Output 1: motherboard:freaking:negative \\  & Example Input 2: I had always used PCs ....cashing and the poorly designed \\  & OS that were never very intuitive \\  & Example Output 2: OS:poorly designed, OS: never very intuitive \\ \hline \end{tabular}
\end{table}
Table 26: Illustrating Definition + 2 negative exemplars based prompting for AOPE subtask

\begin{table}
\begin{tabular}{p{43.7pt} p{59.8pt}} \hline
* 1 Positive, Negative and Neutral Example \\ \hline
**Definition** & Definition: The output will be the aspect terms in the \\  & sentence followed by its describing/opinion term. \\ \hline
**Positive** & Example Input 1: I charge it at night and skip taking the cord with me because of the good battery life. \\  & Example Output 1: battery life:good \\ \hline
**Negative** & Example Input 1: A month or so ago , the freaking motherboard just died . \\
**Example** & Example Output 1: motherboard:freaking \\ \hline
**Neutral** & Example Input 1: It has a 10 hour ... when you ’re doing web browsing and word editing , making it perfect for the classroom or office, ... \\  & Example Output 1: web browsing:perfect, word editing:perfect \\ \hline
**Input** & Now complete the following example- \\  & input: Boot time is super fast , around anywhere from 35 seconds to 1 minute \\ \hline \end{tabular}
\end{table}
Table 27: Illustrating Definition + 2 neutral exemplars based prompting for AOPE subtask

\begin{table}
\begin{tabular}{p{43.7pt} p{59.8pt}} \hline
* 2 Neutral Examples \\ \hline
**Definition** & Definition: The output will be the aspect terms in the \\  & sentence followed by its describing/opinion term. \\ \hline
**Negative** & Example Input 1: A month or so ago , the freaking motherboard just died . \\
**Example** & Example Output 1: motherboard:freaking \\ \hline
**Negative** & Example Input 1: It has a 10 hour ... when you ’re doing web browsing and word editing , making it perfect for the classroom or office, ... \\  & Example Output 1: web browsing:perfect, word editing:perfect \\ \hline
**Input** & Now complete the following example- \\  & input: Boot time is super fast , around anywhere from 35 seconds to 1 minute \\  & output: Boot time:superfast \\ \hline \end{tabular}
\end{table}
Table 25: Illustrating Only Task Definition based prompting for AOPE subtask.





\begin{table}
\begin{tabular}{l l} \hline
* Delusive Examples \\ \hline
**Definition** & Definition: The output will be the aspect terms in the sentence followed by its describing/opinion term. \\ \hline
**Positive** & Example Input 1: I charge it at night and skip taking the cord with me because of the good battery life. \\  & Example Output 1: motherboard:freaking \\ \hline
**Negative** & Example Input 1: A month or so ago , the freaking motherboard just died . \\
**Example** & Example Output 1: web browsing:perfect, word editing:perfect \\ \hline
**Neutral** & Example Input 1: It has a 10 hour ... when you ’re doing web browsing and word editing , making it perfect for the classroom or office, ... \\  & Example Output 1: battery life:good \\ \hline
**Input** & Now complete the following example- \\  & input: Boot time is super fast , around anywhere from 35 seconds to 1 minute \\  & output: Mac M1: fast \\ \hline \end{tabular}
\end{table}
Table 29: Illustrating delusive instruction based prompting for AOPE subtask. In this task, the output labels of the examplars are mapped incorrectly with the inputs.