小心解冻：空间有效的语义解析模型微调
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models
摘要：
语义解析是自然语言处理的一项关键任务，它将自然语言映射为结构化的语义表示。与许多其他NLP任务一样，SOTA在语义解析中的性能现在是通过微调大型预训练语言模型（PLM）来实现的。虽然有效，但这种方法在存在多个下游任务的情况下是低效的，因为需要为每个任务单独存储PLM的所有参数的一组新值。最近的工作探索了使PLM适应下游任务的方法，同时保持其大部分（或全部）参数不变。我们研究了两个这样有前途的技术，前缀调整和偏置项调整，特别是在语义解析。我们在两个不同的语义解析数据集上对它们进行了比较，并且在Few-Shot和传统数据设置中对它们进行了全面和部分微调。虽然前缀调优表现不佳的语义解析任务现成的，我们修改它添加特殊的令牌嵌入，这将导致非常强大的性能，而不影响参数的节省。
英文摘要：
Semantic parsing is a key NLP task that maps natural language to structured meaning representations. As in many other NLP tasks, SOTA performance in semantic parsing is now attained by fine-tuning a large pretrained language model (PLM). While effective, this approach is inefficient in the presence of multiple downstream tasks, as a new set of values for all parameters of the PLM needs to be stored for each task separately. Recent work has explored methods for adapting PLMs to downstream tasks while keeping most (or all) of their parameters frozen. We examine two such promising techniques, prefix tuning and bias-term tuning, specifically on semantic parsing. We compare them against each other on two different semantic parsing datasets, and we also compare them against full and partial fine-tuning, both in few-shot and conventional data settings. While prefix tuning is shown to do poorly for semantic parsing tasks off the shelf, we modify it by adding special token embeddings, which results in very strong performance without compromising parameter savings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
