Do We Need to Create Big Datasets to Learn a Task? Article Swipe
Swaroop Mishra
,
Bhavdeep Sachdeva
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2020.sustainlp-1.23
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2020.sustainlp-1.23
Deep Learning research has been largely accelerated by the development of huge datasets such as Imagenet. The general trend has been to create big datasets to make a deep neural network learn. A huge amount of resources is being spent in creating these big datasets, developing models, training them, and iterating this process to dominate leaderboards. We argue that the trend of creating bigger datasets needs to be revised by better leveraging the power of pre-trained language models. Since the language models have already been pre-trained with huge amount of data and have basic linguistic knowledge, there is no need to create big datasets to learn a task. Instead, we need to create a dataset that is sufficient for the model to learn various task-specific terminologies, such as ‘Entailment’, ‘Neutral’, and ‘Contradiction’ for NLI. As evidence, we show that RoBERTA is able to achieve near-equal performance on 2% data of SNLI. We also observe competitive zero-shot generalization on several OOD datasets. In this paper, we propose a baseline algorithm to find the optimal dataset for learning a task.
Related Topics
Concepts
Computer science
Task (project management)
Artificial intelligence
Generalization
Baseline (sea)
Process (computing)
Big data
Machine learning
Deep learning
Artificial neural network
Contradiction
Deep neural networks
Natural language processing
Data science
Data mining
Operating system
Economics
Mathematics
Mathematical analysis
Philosophy
Management
Oceanography
Geology
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2020.sustainlp-1.23
- https://www.aclweb.org/anthology/2020.sustainlp-1.23.pdf
- OA Status
- gold
- Cited By
- 16
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3104537233
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3104537233Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2020.sustainlp-1.23Digital Object Identifier
- Title
-
Do We Need to Create Big Datasets to Learn a Task?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Swaroop Mishra, Bhavdeep SachdevaList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2020.sustainlp-1.23Publisher landing page
- PDF URL
-
https://www.aclweb.org/anthology/2020.sustainlp-1.23.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.aclweb.org/anthology/2020.sustainlp-1.23.pdfDirect OA link when available
- Concepts
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Computer science, Task (project management), Artificial intelligence, Generalization, Baseline (sea), Process (computing), Big data, Machine learning, Deep learning, Artificial neural network, Contradiction, Deep neural networks, Natural language processing, Data science, Data mining, Operating system, Economics, Mathematics, Mathematical analysis, Philosophy, Management, Oceanography, Geology, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 5, 2022: 4, 2021: 6Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.SNLI. | 150 |
| abstract_inverted_index.Since | 78 |
| abstract_inverted_index.argue | 57 |
| abstract_inverted_index.basic | 93 |
| abstract_inverted_index.being | 38 |
| abstract_inverted_index.learn | 105, 122 |
| abstract_inverted_index.model | 120 |
| abstract_inverted_index.needs | 65 |
| abstract_inverted_index.power | 73 |
| abstract_inverted_index.spent | 39 |
| abstract_inverted_index.task. | 107, 177 |
| abstract_inverted_index.them, | 48 |
| abstract_inverted_index.there | 96 |
| abstract_inverted_index.these | 42 |
| abstract_inverted_index.trend | 18, 60 |
| abstract_inverted_index.amount | 34, 88 |
| abstract_inverted_index.better | 70 |
| abstract_inverted_index.bigger | 63 |
| abstract_inverted_index.create | 22, 101, 112 |
| abstract_inverted_index.learn. | 31 |
| abstract_inverted_index.models | 81 |
| abstract_inverted_index.neural | 29 |
| abstract_inverted_index.paper, | 163 |
| abstract_inverted_index.RoBERTA | 139 |
| abstract_inverted_index.achieve | 143 |
| abstract_inverted_index.already | 83 |
| abstract_inverted_index.dataset | 114, 173 |
| abstract_inverted_index.general | 17 |
| abstract_inverted_index.largely | 5 |
| abstract_inverted_index.models, | 46 |
| abstract_inverted_index.models. | 77 |
| abstract_inverted_index.network | 30 |
| abstract_inverted_index.observe | 153 |
| abstract_inverted_index.optimal | 172 |
| abstract_inverted_index.process | 52 |
| abstract_inverted_index.propose | 165 |
| abstract_inverted_index.revised | 68 |
| abstract_inverted_index.several | 158 |
| abstract_inverted_index.various | 123 |
| abstract_inverted_index.Instead, | 108 |
| abstract_inverted_index.Learning | 1 |
| abstract_inverted_index.baseline | 167 |
| abstract_inverted_index.creating | 41, 62 |
| abstract_inverted_index.datasets | 12, 24, 64, 103 |
| abstract_inverted_index.dominate | 54 |
| abstract_inverted_index.language | 76, 80 |
| abstract_inverted_index.learning | 175 |
| abstract_inverted_index.research | 2 |
| abstract_inverted_index.training | 47 |
| abstract_inverted_index.Imagenet. | 15 |
| abstract_inverted_index.algorithm | 168 |
| abstract_inverted_index.datasets, | 44 |
| abstract_inverted_index.datasets. | 160 |
| abstract_inverted_index.evidence, | 135 |
| abstract_inverted_index.iterating | 50 |
| abstract_inverted_index.resources | 36 |
| abstract_inverted_index.zero-shot | 155 |
| abstract_inverted_index.developing | 45 |
| abstract_inverted_index.knowledge, | 95 |
| abstract_inverted_index.leveraging | 71 |
| abstract_inverted_index.linguistic | 94 |
| abstract_inverted_index.near-equal | 144 |
| abstract_inverted_index.sufficient | 117 |
| abstract_inverted_index.accelerated | 6 |
| abstract_inverted_index.competitive | 154 |
| abstract_inverted_index.development | 9 |
| abstract_inverted_index.performance | 145 |
| abstract_inverted_index.pre-trained | 75, 85 |
| abstract_inverted_index.leaderboards. | 55 |
| abstract_inverted_index.task-specific | 124 |
| abstract_inverted_index.generalization | 156 |
| abstract_inverted_index.terminologies, | 125 |
| abstract_inverted_index.‘Neutral’, | 129 |
| abstract_inverted_index.‘Entailment’, | 128 |
| abstract_inverted_index.‘Contradiction’ | 131 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.89769238 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |