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arXiv · 2609.06108

From Two Passes to One: Compact and Efficient Target-Stance Extraction

Ethan Mines·Bonnie Dorr
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Abstract

Target-Stance Extraction (TSE) is the task of predicting both the target (or topic) of an author's writing and the author's stance toward it. Existing approaches to TSE use a sequential pipeline of two separate neural models: one to identify the target and another to determine the stance. We present a one-pass, joint architecture that predicts both in a single forward pass, reducing trainable parameters by nearly 50% with only a 4-7 F1 point tradeoff in performance. We further demonstrate that standard target-scrubbing practices artificially suppress target prediction accuracy. Retaining explicit target mentions, as in real-world deployments, improves F1 by at least 6 points across both target classification and target generation settings. These improvements allow for significantly easier integration of TSE in downstream applications such as public opinion tracking.

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Ethan Mines, Bonnie Dorr. 2026-09-05. From Two Passes to One: Compact and Efficient Target-Stance Extraction. https://arxiv.org/abs/2609.06108

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