Rethinking Crowd Sourcing for Semantic Similarity
Computation and Language
2021-09-27 v1 Artificial Intelligence
Human-Computer Interaction
Abstract
Estimation of semantic similarity is crucial for a variety of natural language processing (NLP) tasks. In the absence of a general theory of semantic information, many papers rely on human annotators as the source of ground truth for semantic similarity estimation. This paper investigates the ambiguities inherent in crowd-sourced semantic labeling. It shows that annotators that treat semantic similarity as a binary category (two sentences are either similar or not similar and there is no middle ground) play the most important role in the labeling. The paper offers heuristics to filter out unreliable annotators and stimulates further discussions on human perception of semantic similarity.
Cite
@article{arxiv.2109.11969,
title = {Rethinking Crowd Sourcing for Semantic Similarity},
author = {Shaul Solomon and Adam Cohn and Hernan Rosenblum and Chezi Hershkovitz and Ivan P. Yamshchikov},
journal= {arXiv preprint arXiv:2109.11969},
year = {2021}
}