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Open-source Frame Semantic Parsing
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While the state-of-the-art for frame semantic parsing has progressed dramatically in recent years, it is still difficult for end-users to apply state-of-the-art models in practice. To address this, we present Frame Semantic Transformer, an open-source Python library which achieves near state-of-the-art performance on FrameNet 1.7, while focusing on ease-of-use. We use a T5 model fine-tuned on Propbank and FrameNet exemplars as a base, and improve performance by using FrameNet lexical units to provide hints to T5 at inference time. We enhance robustness to real-world data by using textual data augmentations during training.
Forward citations
Cited by 2 Pith papers
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Fine-tuning open-source LLMs on a prover-filtered preference dataset improves whole-problem translation of natural-language reasoning into first-order logic, cutting syntax errors and increasing logical correctness.
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Beyond the Battlefield: Framing Analysis of Media Coverage in Conflict Reporting
A computational framing analysis finds war-oriented reporting dominates, with US/UK outlets more often framing Hamas as assailant and Middle Eastern outlets framing Israel as assailant and Palestinians as victims.
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