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Attitude Identification through Parameter-Efficient Fine-Tuning
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Attitude Identification through Parameter-Efficient Fine-Tuning
We investigate automatic attitude detection in UN Security Council speeches using adapters. Following Martin and White’s Appraisal Theory, we identify three types of evaluative language: affect (emotional responses such as hope or concern), judgement (ethical evaluations of behavior), and appreciation (valuations of objects or situations). Training only 0.95% of BERT-large’s parameters, adapters achieve F1 scores ranging from 0.76 (affect) to 0.46 (appreciation), approaching full fine-tuning performance while enabling rapid task-specific experimentation. Differences in observed evaluation metrics mirror the pattern of the human inter-annotator agreement. This correlation suggests that computational difficulty reflects genuine linguistic ambiguity. Affect benefits from conventionalized diplomatic expressions, while appreciation faces context-dependent evaluation and severe class imbalance. Analysis demonstrates that evaluative intensity varies systematically across diplomatic contexts, with implications for corpus design in specialized discourse analysis.
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