Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models Article Swipe
Tassilo Klein
,
Moin Nabi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.emnlp-main.688
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.emnlp-main.688
Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast to previous studies that rely on fine-tuning annotated datasets, we seek to boost conceptualization via loss landscape refinement. To this end, we propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships. Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks.
Related Topics
Concepts
Commonsense reasoning
Computer science
Conceptualization
Artificial intelligence
Natural language processing
Schema (genetic algorithms)
Commonsense knowledge
Language model
Zero (linguistics)
Language understanding
Simple (philosophy)
Task (project management)
Machine learning
Knowledge-based systems
Linguistics
Epistemology
Management
Economics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2021.emnlp-main.688
- https://aclanthology.org/2021.emnlp-main.688.pdf
- OA Status
- hybrid
- Cited By
- 4
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3199329183
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3199329183Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2021.emnlp-main.688Digital Object Identifier
- Title
-
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Tassilo Klein, Moin NabiList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2021.emnlp-main.688Publisher landing page
- PDF URL
-
https://aclanthology.org/2021.emnlp-main.688.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://aclanthology.org/2021.emnlp-main.688.pdfDirect OA link when available
- Concepts
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Commonsense reasoning, Computer science, Conceptualization, Artificial intelligence, Natural language processing, Schema (genetic algorithms), Commonsense knowledge, Language model, Zero (linguistics), Language understanding, Simple (philosophy), Task (project management), Machine learning, Knowledge-based systems, Linguistics, Epistemology, Management, Economics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.propose | 67 |
| abstract_inverted_index.refines | 74 |
| abstract_inverted_index.similar | 85 |
| abstract_inverted_index.studies | 47 |
| abstract_inverted_index.Winograd | 28 |
| abstract_inverted_index.analysis | 89 |
| abstract_inverted_index.approach | 72 |
| abstract_inverted_index.contrast | 44 |
| abstract_inverted_index.existing | 3 |
| abstract_inverted_index.language | 4, 41, 76 |
| abstract_inverted_index.learning | 71 |
| abstract_inverted_index.multiple | 103 |
| abstract_inverted_index.presents | 15 |
| abstract_inverted_index.previous | 46 |
| abstract_inverted_index.Challenge | 30 |
| abstract_inverted_index.Empirical | 88 |
| abstract_inverted_index.annotated | 52 |
| abstract_inverted_index.datasets, | 53 |
| abstract_inverted_index.exploring | 19 |
| abstract_inverted_index.framework | 94 |
| abstract_inverted_index.landscape | 61 |
| abstract_inverted_index.reasoning | 25, 101 |
| abstract_inverted_index.utilizing | 78 |
| abstract_inverted_index.viability | 97 |
| abstract_inverted_index.zero-shot | 10, 23, 99 |
| abstract_inverted_index.linguistic | 82 |
| abstract_inverted_index.reasoning? | 12 |
| abstract_inverted_index.refinement | 37 |
| abstract_inverted_index.benchmarks. | 104 |
| abstract_inverted_index.commonsense | 11, 24, 100 |
| abstract_inverted_index.feasibility | 21 |
| abstract_inverted_index.fine-tuning | 51 |
| abstract_inverted_index.formulating | 32 |
| abstract_inverted_index.pre-trained | 40 |
| abstract_inverted_index.refinement. | 62 |
| abstract_inverted_index.conceptually | 92 |
| abstract_inverted_index.demonstrates | 95 |
| abstract_inverted_index.perturbations | 83 |
| abstract_inverted_index.relationships. | 87 |
| abstract_inverted_index.self-supervised | 36, 70 |
| abstract_inverted_index.conceptualization | 58 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.65456751 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |