GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
Shikib Mehri, Maxine Eskénazi · 2021
In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task.Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inherent scalability of the transfer learning paradigm.We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning.We present GENSF (Generative Slot Filling), which leverages a generative pre-trained open-domain dialog model for slot filling.GENSF (1) adapts the pre-trained model by incorporating inductive biases about the task and (2) adapts the downstream task by reformulating slot filling to better leverage the pre-trained model's capabilities.GENSF achieves state-of-the-art results on two slot filling datasets with strong gains in few-shot and zero-shot settings.We achieve a 9 F 1 score improvement in zeroshot slot filling.This highlights the value of strong alignment between the pre-trained model and the downstream task.