Dataset Card for climate-fever-nli-stsb

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.

Usage

Load the cf-nli dataset

# if datasets not already in your environment
!pip install datasets

from datasets import load_dataset

# all splits...
dd = load_dataset('climate-fever-nli-stsb', 'cf-nli')

# ... or specific split (only 'train' is available)
ds_train = load_dataset('climate-fever-nli-stsb', 'cf-nli', split='train')

## ds_train can now be injected into SentenceBERT training scripts at the point
## where individual sentence pairs are aggregated into
## {'claim': {'entailment': set(), 'contradiction': set(), 'neutral': set()}} dicts
## for further processing into training samples

Load the cf-nli-nei dataset

# if datasets not already in your environment
!pip install datasets

from datasets import load_dataset

# all splits...
dd = load_dataset('climate-fever-nli-stsb', 'cf-nli-nei')

# ... or specific split (only 'train' is available)
ds_train = load_dataset('climate-fever-nli-stsb', 'cf-nli-nei', split='train')

## ds_train can now be injected into SentenceBERT training scripts at the point
## where individual sentence pairs are aggregated into
## {'claim': {'entailment': set(), 'contradiction': set(), 'neutral': set()}} dicts
## for further processing into training samples

Load the cf-stsb dataset

# if datasets not already in your environment
!pip install datasets

from datasets import load_dataset

# all splits...
dd = load_dataset('climate-fever-nli-stsb', 'cf-stsb')

# ... or specific split ('train', 'dev', 'test' available)
ds_dev = load_dataset('climate-fever-nli-stsb', 'cf-stsb', split='dev')

## ds_dev (or test) can now be injected into SentenceBERT training scripts at the point
## where individual sentence pairs are aggregated into
## a list of dev (or test) samples

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

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

Annotation process

cf-nli

For each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset:

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.

votes	effective evidence_label
SUPPORTS > REFUTES	SUPPORTS
SUPPORTS < REFUTES	REFUTES

In addition to all the claims in cf-nli, 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.

cf-stsb

For each Claim <-> Evidence pair, create labeled pairs in the style of STSb dataset:

split	dataset	score	sentence1	sentence2
{'train', 'dev', 'test'}	'climate-fever'	cos_sim score	claim	evidence

This dataset uses 'evidence_label', vote 'entropy', and the list of annotator 'votes' to derive a similarity score for each claim <-> evidence pairing. Similarity score conversion:

mean(entropy) refers to the average entropy within the defined group of evidence

evidence_label	votes	similarity score
SUPPORTS	SUPPORTS > 0, REFUTES == 0, NOT_ENOUGH_INFO (NEI) == 0	1
	SUPPORTS > 0, REFUTES == 0	mean(entropy)
	SUPPORTS > 0, REFUTES > 0	1 - mean(entropy)
NEI	SUPPORTS > REFUTES	(1 - mean(entropy)) / 2
	SUPPORTS == REFUTES	0
	SUPPORTS < REFUTES	-(1 - mean(entropy)) / 2
REFUTES	SUPPORTS > 0, REFUTES > 0	-(1 - mean(entropy))
	SUPPORTS == 0, REFUTES > 0	-mean(entropy)
	SUPPORTS == 0, REFUTES > 0, NEI == 0	-1

The above derivation roughly maps the strength of evidence annotation (REFUTES..NEI..SUPPORTS) to cosine similarity (-1..0..1).