Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation

Aparna Elangovan, Jiayuan He, Karin Verspoor


Abstract
Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). However, the presence of overlap between the train and test datasets can lead to inflated results, inadvertently evaluating the model’s ability to memorize and interpreting it as the ability to generalize. In addition, such data sets may not provide an effective indicator of the performance of these methods in real world scenarios. We identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction, and study them to assess the impact of that leakage on the model’s ability to memorize versus generalize.
Anthology ID:
2021.eacl-main.113
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1325–1335
Language:
URL:
https://aclanthology.org/2021.eacl-main.113
DOI:
10.18653/v1/2021.eacl-main.113
Bibkey:
Cite (ACL):
Aparna Elangovan, Jiayuan He, and Karin Verspoor. 2021. Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1325–1335, Online. Association for Computational Linguistics.
Cite (Informal):
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation (Elangovan et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.113.pdf
Data
GLUESSTSST-2emrQA