InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis Article Swipe
Kevin Scaria
,
Himanshu Gupta
,
Siddharth Goyal
,
Saurabh Arjun Sawant
,
Swaroop Mishra
,
Chitta Baral
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.08624
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.08624
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 (Tk-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 also get competitive results on AOOE, AOPE, and AOSTE 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 ~10% when adding misleading examples.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.08624
- https://arxiv.org/pdf/2302.08624
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321392371
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4321392371Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.08624Digital Object Identifier
- Title
-
InstructABSA: Instruction Learning for Aspect Based Sentiment AnalysisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-16Full publication date if available
- Authors
-
Kevin Scaria, Himanshu Gupta, Siddharth Goyal, Saurabh Arjun Sawant, Swaroop Mishra, Chitta BaralList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.08624Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.08624Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.08624Direct OA link when available
- Concepts
-
Generalization, Computer science, Sample (material), Artificial intelligence, Sentiment analysis, Machine learning, Term (time), Quality (philosophy), Natural language processing, Mathematics, Epistemology, Mathematical analysis, Physics, Chromatography, Chemistry, Quantum mechanics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.introduce | 1 |
| abstract_inverted_index.negative, | 17 |
| abstract_inverted_index.positive, | 16 |
| abstract_inverted_index.subtasks, | 33 |
| abstract_inverted_index.subtasks. | 12, 69, 122 |
| abstract_inverted_index.Extraction | 60, 67 |
| abstract_inverted_index.approaches | 57 |
| abstract_inverted_index.efficiency | 125 |
| abstract_inverted_index.indicating | 116 |
| abstract_inverted_index.introduces | 15 |
| abstract_inverted_index.misleading | 162 |
| abstract_inverted_index.surpassing | 101 |
| abstract_inverted_index.approaches. | 142 |
| abstract_inverted_index.competitive | 108, 136 |
| abstract_inverted_index.demonstrate | 49 |
| abstract_inverted_index.experiences | 155 |
| abstract_inverted_index.instruction | 4, 26, 140 |
| abstract_inverted_index.outperforms | 52, 73 |
| abstract_inverted_index.particular, | 71 |
| abstract_inverted_index.performance | 36, 154 |
| abstract_inverted_index.significant | 35 |
| abstract_inverted_index.Aspect-Based | 8 |
| abstract_inverted_index.Experimental | 38 |
| abstract_inverted_index.InstructABSA | 51, 72 |
| abstract_inverted_index.instructions | 149 |
| abstract_inverted_index.(Tk-Instruct) | 30 |
| abstract_inverted_index.InstructABSA, | 2 |
| abstract_inverted_index.improvements. | 37 |
| abstract_inverted_index.InstructABSA's | 153 |
| abstract_inverted_index.generalization | 118 |
| abstract_inverted_index.state-of-the-art | 55, 76 |
| abstract_inverted_index.Classification(ATSC) | 63 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |