The Power of Prompt Tuning for Low-Resource Semantic Parsing
This work addresses the challenge of semantic parsing with limited data, showing incremental improvements in performance for NLP applications.
The paper tackled the problem of adapting pre-trained language models to low-resource semantic parsing, and found that prompt-tuned T5-xl outperformed fine-tuned models and baselines on datasets like Overnight and TOPv2.
Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and generation tasks. In this paper, we investigate prompt tuning for semantic parsing -- the task of mapping natural language utterances onto formal meaning representations. On the low-resource splits of Overnight and TOPv2, we find that a prompt tuned T5-xl significantly outperforms its fine-tuned counterpart, as well as strong GPT-3 and BART baselines. We also conduct ablation studies across different model scales and target representations, finding that, with increasing model scale, prompt tuned T5 models improve at generating target representations that are far from the pre-training distribution.