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Dataset Card for climate-fever-nli-triplets
Dataset Description
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Dataset Summary
The CLIMATE-FEVER dataset modified to supply NLI-style (cf-nli) features or STSb-style (cf-stsb) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets.
There are two cf-nli datasets: one derived from only SUPPORTS and REFUTES evidence (cf-nli), and one that also derived data from NOT_ENOUGH_INFO evidence based on the annotator votes (cf-nli-nei).
The feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for load_dataset examples.
Supported Tasks and Leaderboards
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Languages
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Dataset Structure
Data Instances
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Data Fields
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Data Splits
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Dataset Creation
Curation Rationale
SentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, intrinsic STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets.
So, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other labeled datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT.
This modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge intrinsic model performance on STSb.
Source Data
Initial Data Collection and Normalization
see CLIMATE-FEVER
Who are the source language producers?
see CLIMATE-FEVER
Annotations
Annotation process
NLI Derivation
cf-nli
For each Claim that has both SUPPORTS evidence and REFUTES evidence, create the following labeled pairs in the style of NLI dataset:
NLI Fields
| split | dataset | sentence1 | sentence2 | label |
|---|---|---|---|---|
| {'train', 'test'} | 'climate-fever' | claim | evidence | evidence_label SUPPORTS -> 'entailment', REFUTES -> 'contradiction' |
Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER.
cf-nli-nei
This dataset uses the list of annotator 'votes' to cast a NOT_ENOUGH_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs.
Casting NEI Evidence to SUPPORTS or REFUTES
| votes | effective evidence_label |
|---|---|
| SUPPORTS > REFUTES | SUPPORTS |
| SUPPORTS < REFUTES | REFUTES |
Any Claims that have,
- at least one SUPPORTS or REFUTES evidence, AND
- NEI evidences that can be cast to effective SUPPORTS or REFUTES
are included in the datasset.
STSb Derivation
TBD
Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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Discussion of Biases
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Other Known Limitations
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Additional Information
Dataset Curators
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Licensing Information
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Citation Information
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Contributions
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