Realistic Data Augmentation Framework for Enhancing Tabular Reasoning Article Swipe
Dibyakanti Kumar
,
Vivek Gupta
,
Soumya Sharma
,
Shuo Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2022.findings-emnlp.324
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2022.findings-emnlp.324
Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time consuming and thus limits scale, and the latter often produces naive examples that may lack complex reasoning. This paper develops a realistic semi-automated framework for data augmentation for tabular inference. Instead of manually generating a hypothesis for each table, our methodology generates hypothesis templates transferable to similar tables. In addition, our framework entails the creation of rational counterfactual tables based on human written logical constraints and premise paraphrasing. For our case study, we use the INFOTABS (Gupta et al., 2020), which is an entity centric tabular inference dataset. We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2022.findings-emnlp.324
- https://aclanthology.org/2022.findings-emnlp.324.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385574350
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385574350Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2022.findings-emnlp.324Digital Object Identifier
- Title
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Realistic Data Augmentation Framework for Enhancing Tabular ReasoningWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Dibyakanti Kumar, Vivek Gupta, Soumya Sharma, Shuo ZhangList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2022.findings-emnlp.324Publisher landing page
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https://aclanthology.org/2022.findings-emnlp.324.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://aclanthology.org/2022.findings-emnlp.324.pdfDirect OA link when available
- Concepts
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Computer science, Inference, Premise, Table (database), Counterfactual thinking, Crowdsourcing, Artificial intelligence, Machine learning, Natural language processing, Data mining, Philosophy, Linguistics, Epistemology, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.centric | 117 |
| abstract_inverted_index.complex | 48 |
| abstract_inverted_index.entails | 85 |
| abstract_inverted_index.limited | 143 |
| abstract_inverted_index.logical | 96 |
| abstract_inverted_index.premise | 99 |
| abstract_inverted_index.similar | 79 |
| abstract_inverted_index.tables. | 80 |
| abstract_inverted_index.tabular | 61, 118, 129 |
| abstract_inverted_index.written | 95 |
| abstract_inverted_index.Existing | 0 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.INFOTABS | 108 |
| abstract_inverted_index.Language | 8 |
| abstract_inverted_index.creation | 87 |
| abstract_inverted_index.dataset. | 120 |
| abstract_inverted_index.develops | 52 |
| abstract_inverted_index.examples | 44 |
| abstract_inverted_index.generate | 127 |
| abstract_inverted_index.manually | 65 |
| abstract_inverted_index.methods. | 25 |
| abstract_inverted_index.observed | 122 |
| abstract_inverted_index.produces | 42 |
| abstract_inverted_index.rational | 89 |
| abstract_inverted_index.scenario | 141 |
| abstract_inverted_index.training | 4, 135 |
| abstract_inverted_index.Inference | 9 |
| abstract_inverted_index.addition, | 82 |
| abstract_inverted_index.automatic | 24 |
| abstract_inverted_index.consuming | 33 |
| abstract_inverted_index.examples, | 131 |
| abstract_inverted_index.expensive | 30 |
| abstract_inverted_index.framework | 56, 84, 125 |
| abstract_inverted_index.generates | 74 |
| abstract_inverted_index.inference | 119, 130 |
| abstract_inverted_index.realistic | 54 |
| abstract_inverted_index.templates | 76 |
| abstract_inverted_index.approaches | 1 |
| abstract_inverted_index.especially | 138 |
| abstract_inverted_index.generating | 66 |
| abstract_inverted_index.human-like | 128 |
| abstract_inverted_index.hypothesis | 68, 75 |
| abstract_inverted_index.inference. | 62 |
| abstract_inverted_index.reasoning, | 17 |
| abstract_inverted_index.reasoning. | 49 |
| abstract_inverted_index.constraints | 97 |
| abstract_inverted_index.methodology | 73 |
| abstract_inverted_index.augmentation | 59 |
| abstract_inverted_index.constructing | 3 |
| abstract_inverted_index.supervision. | 144 |
| abstract_inverted_index.transferable | 77 |
| abstract_inverted_index.augmentation, | 137 |
| abstract_inverted_index.crowdsourcing | 21 |
| abstract_inverted_index.paraphrasing. | 100 |
| abstract_inverted_index.counterfactual | 90 |
| abstract_inverted_index.semi-automated | 55 |
| abstract_inverted_index.semi-structured | 15 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.6557715 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |