Datasets:
language:
- am
- ha
- en
- es
- te
- ar
- af
license: mit
size_categories:
- 10K<n<100K
tags:
- Semantic Textual Relatedness
dataset_info:
features:
- name: PairID
dtype: string
- name: Language
dtype: string
- name: Sentence1
dtype: string
- name: Sentence2
dtype: string
- name: Length
dtype: int64
- name: Score
dtype: float64
splits:
- name: train
num_bytes: 3987243
num_examples: 14345
- name: dev
num_bytes: 434051
num_examples: 1295
download_size: 2227282
dataset_size: 4421294
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
Dataset Card for Dataset Name
Dataset Details
Dataset Description
Each instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper.