Prompt-based Few-shot Learning for Table-based Fact Verification
Abstract
References
Index Terms
- Prompt-based Few-shot Learning for Table-based Fact Verification
Recommendations
Joint contrastive learning for prompt-based few-shot language learners
AbstractThe combination of prompt learning and contrastive learning has recently been a promising approach to few-shot learning in NLP field. However, most of these studies only focus on the semantic-level relevance and intra-class information of data in ...
Domain-control prompt-driven zero-shot relational triplet extraction
AbstractZero-shot relational triplet extraction is a vital solution to the problem of fact extracted from unstructured text without labeled training data. In the task, the data is divided into seen and unseen relations for training and prediction, ...
Highlights- Prompts are able to control the output domain, guiding the extraction of unseen triplets.
- Potential relations can be determined according to semantic matching between sentences and relation description text.
- A two-stage framework ...
OpenTFV: An Open Domain Table-Based Fact Verification System
SIGMOD '22: Proceedings of the 2022 International Conference on Management of DataThe prevalence of misinformation, both online and offline, has prompted a great demand of fact verification. Table-based fact verification aims to check whether a textual claim is supported or refuted based on relational tables. However, most of the ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Other conferences](/cms/asset/96392968-68a4-4dc1-9c7d-fbf37c7ec3c7/3578741.cover.jpg)
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 120Total Downloads
- Downloads (Last 12 months)60
- Downloads (Last 6 weeks)1
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format