Adversarial Analysis of Natural Language Inference Systems
TABLE VI: Results for non-entailment subcases. Each row corresponds to a syntactic phenomenon. BERT large UP: trained on unparsed then parsed; MT DNN-base PO: trained on parsed only
['Heuristic', 'Syntactic subcategory', 'MT-DNN large', 'BERT large', 'MT-DNN base', 'BERT base', 'ESIM', 'BERT large UP', 'MT-DNN base PO']
Considering that syntactic phenomena are one of the models’ weaknesses, we conduct an experiment of simply passing the flattened binary parses as the input “sentences”. We use the automatically generated parses that come with MNLI and the adversarial dataset. We find that it is rather difficult to get the different models to train well. Some had loss that never converged, some got near 0% on all non-entailment subcases. The only reasonable parsed models are BERTlarge under the first regimen (UP), and MT-DNNbase under the second (PO). It is likely that these difficulties could be overcome with some systematic hyperparameter tuning, but we see substantial consistency (in model performance on the adversarial dataset) between the two successes, so do not think it would be very insightful to test more. But the fact that the models responded so differently to fine-tuning suggests that the models have significantly different ‘knowledge states’ in terms of what they learned about how to solve tasks, i.e. they ended up in different local optima after pre-training. This idea deserves more analysis, because the whole point of huge pre-training is to learn maximally transferable and general representations of language. Thus, how to guide models towards these ideal local optima (and away from overfitting) is a very important and difficult question.