Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning
This is an incremental improvement for NLP practitioners working on NLI tasks, as it enhances fine-tuning efficiency without additional neural network computation.
The paper tackles the problem of improving fine-tuning performance on small Natural Language Inference datasets by replacing the standard MLP classification head with Gradient Boosted Decision Trees, resulting in consistent improvements over the baseline model.
Transfer learning has become the dominant paradigm for many natural language processing tasks. In addition to models being pretrained on large datasets, they can be further trained on intermediate (supervised) tasks that are similar to the target task. For small Natural Language Inference (NLI) datasets, language modelling is typically followed by pretraining on a large (labelled) NLI dataset before fine-tuning with each NLI subtask. In this work, we explore Gradient Boosted Decision Trees (GBDTs) as an alternative to the commonly used Multi-Layer Perceptron (MLP) classification head. GBDTs have desirable properties such as good performance on dense, numerical features and are effective where the ratio of the number of samples w.r.t the number of features is low. We then introduce FreeGBDT, a method of fitting a GBDT head on the features computed during fine-tuning to increase performance without additional computation by the neural network. We demonstrate the effectiveness of our method on several NLI datasets using a strong baseline model (RoBERTa-large with MNLI pretraining). The FreeGBDT shows a consistent improvement over the MLP classification head.