Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds

John P. Lalor, Hao Wu, Hong Yu


Abstract
Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a significant bottleneck for large data sets like those required for training deep neural networks (DNNs). In this work we propose learning IRT models using RPs generated from artificial crowds of DNN models. We demonstrate the effectiveness of learning IRT models using DNN-generated data through quantitative and qualitative analyses for two NLP tasks. Parameters learned from human and machine RPs for natural language inference and sentiment analysis exhibit medium to large positive correlations. We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods. Finally, we highlight cases where human expectation about item difficulty does not match difficulty as estimated from the machine RPs.
Anthology ID:
D19-1434
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4249–4259
Language:
URL:
https://aclanthology.org/D19-1434
DOI:
10.18653/v1/D19-1434
Bibkey:
Cite (ACL):
John P. Lalor, Hao Wu, and Hong Yu. 2019. Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4249–4259, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds (Lalor et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1434.pdf
Attachment:
 D19-1434.Attachment.zip
Code
 jplalor/py-irt
Data
SNLI