When data permutations are pathological: the case of neural natural language inference

Natalie Schluter, Daniel Varab


Abstract
Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline. Suppose that by just permuting the examples of the training set, say by reversing the original order, by shuffling, or by mini-batching, you could report substantially better/worst performance for the system of your choice, by multiple percentage points. In this paper, we illustrate this scenario for a trending NLP task: Natural Language Inference (NLI). We show that for the two central NLI corpora today, the learning process of neural systems is far too sensitive to permutations of the data. In doing so we reopen the question of how to judge a good neural architecture for NLI, given the available dataset and perhaps, further, the soundness of the NLI task itself in its current state.
Anthology ID:
D18-1534
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4935–4939
Language:
URL:
https://www.aclweb.org/anthology/D18-1534
DOI:
10.18653/v1/D18-1534
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1534.pdf