Deriving structured semantic representations from unrestricted text, in a format suitable for sound, explainable reasoning, is an important goal for achieving AGI. Consequently much effort has been invested in this goal, but the proposed representations fall short in various ways. Unscoped Logical Form (ULF) is a strictly typed, loss-free semantic representation close to surface form and conducive to linguistic inference. ULF can be further resolved into the more precise Episodic Logic. Previous transformer language models have shown promise in the task of parsing English to ULF, but suffered from a lack of a substantial dataset for training. We present a new fine-tuned language model parser for ULF, trained on a greatly expanded dataset of ULFs automatically derived from Brown corpus Treebank parse trees. Additionally, the model uses Parameter Efficient Fine Tuning (PEFT) to leverage a substantially larger base model than its predecessor while maintaining fast training times. We find that training on automatically derived ULFs substantially improves parser performance from the existing smaller dataset (from SEMBLEU score of 0.43 to 0.68), or even the previously used larger, generatively augmented ULF dataset, used with a transition parser (from SEMBLEU score of 0.49 to 0.68).