Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference
This work addresses a practical problem for researchers and practitioners in NLP by enabling faster model selection, though it is incremental as it builds on existing pre-trained models and similarity methods.
The paper tackles the problem of selecting the best model for abductive natural language inference (αNLI), a difficult NLI task requiring common sense, by proposing a simple performance prediction method that uses cosine similarity on pre-trained embeddings to estimate model accuracy without fine-tuning, achieving a Pearson correlation coefficient of 0.65 and reducing computation time from hours to less than a minute.
The task of abductive natural language inference (αnli), to decide which hypothesis is the more likely explanation for a set of observations, is a particularly difficult type of NLI. Instead of just determining a causal relationship, it requires common sense to also evaluate how reasonable an explanation is. All recent competitive systems build on top of contextualized representations and make use of transformer architectures for learning an NLI model. When somebody is faced with a particular NLI task, they need to select the best model that is available. This is a time-consuming and resource-intense endeavour. To solve this practical problem, we propose a simple method for predicting the performance without actually fine-tuning the model. We do this by testing how well the pre-trained models perform on the αnli task when just comparing sentence embeddings with cosine similarity to what the performance that is achieved when training a classifier on top of these embeddings. We show that the accuracy of the cosine similarity approach correlates strongly with the accuracy of the classification approach with a Pearson correlation coefficient of 0.65. Since the similarity computation is orders of magnitude faster to compute on a given dataset (less than a minute vs. hours), our method can lead to significant time savings in the process of model selection.