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Continual table semantic parsing aims to train a parser on a sequence oftasks, where each task requires the parser to translate natural language intoSQL based on task-specific tables but only offers limited training examples.Conventional methods tend to suffer from overfitting with limited supervision,as well as catastrophic forgetting due to parameter updates. Despite recentadvancements that partially alleviate these issues through semi-supervised dataaugmentation and retention of a few past examples, the performance is stilllimited by the volume of unsupervised data and stored examples. To overcomethese challenges, this paper introduces a novel method integrating\textit{parameter-efficient fine-tuning} (PEFT) and \textit{in-context tuning}(ICT) for training a continual table semantic parser. Initially, we present atask-adaptive PEFT framework capable of fully circumventing catastrophicforgetting, which is achieved by freezing the pre-trained model backbone andfine-tuning small-scale prompts. Building on this, we propose a teacher-studentframework-based solution. The teacher addresses the few-shot problem using ICT,which procures contextual information by demonstrating a few training examples.In turn, the student leverages the proposed PEFT framework to learn from theteacher's output distribution, and subsequently compresses and saves thecontextual information to the prompts, eliminating the need to store anytraining examples. Experimental evaluations on two benchmarks affirm thesuperiority of our method over prevalent few-shot and continual learningbaselines across various metrics.

Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing