From Two Passes to One: Compact and Efficient Target-Stance Extraction
cs.CL
Quick verdict
One-pass TSE model halves parameters at 4-7 F1 cost and shows target-scrubbing suppresses accuracy; convincing, but small wins lack significance tests.
Summary
This paper targets Target-Stance Extraction (TSE), where a system must predict both the target of a document and the author's stance toward it. Prior work (Li et al., 2023) used a two-model pipeline; the authors propose one-pass joint architectures for both benchmark scenarios: for Target Classification, a single BERTweet with two classifier heads sharing the [CLS] representation, and for Target Generation, a BART model augmented with an attention layer whose query is the mean decoder state, feeding a stance head. On the Li et al. benchmark (SemEval-2016, Argument Mining, COVID-19-Stance, P-Stance, plus Unrelated; 18 targets), the one-pass systems cut trainable parameters by 49.89% (TC) and 48.32% (TG) with TSE F1 1.5-6.4 points below their two-pass counterparts under predicted targets. The second contribution is a preprocessing audit: the standard practice of scrubbing target mentions suppresses performance — retaining mentions lifts TC target-prediction F1 from 76.19 to 84.67 (two-pass) and rescues TG from 25.96 to 49.32. They also find a casing bug that neutralized Li et al.'s #SemST-removal regex. Key limitations: results average three seeds with no variance or significance tests; the stance-only proxy (GT-target evaluation, Table 4) shows larger one-pass gaps (e.g., TG 64.13 vs 79.49); the TG comparison rests on an unverified assumption about Li et al.'s scrubbing; and 'efficiency' is supported only by parameter counts, not latency.
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Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- A single model can predict both target and stance in one forward pass for both TSE scenarios, cutting trainable parameters by 49.89% (TC) and 48.32% (TG) (Tables 5, 2-3).
- Under predicted targets, one-pass TSE F1 stays within roughly 1.5-6.4 points of the two-pass counterparts (Tables 2-3).
- Standard target-scrubbing artificially suppresses accuracy; retaining explicit target mentions improves F1 by at least 6 points across TC and TG (e.g., TC 76.19→84.67, Table 2).
- Li et al.'s (2023) #SemST-removal regex was ineffective due to a casing error, so their reported TC baselines include the hashtag cue (§4.2).
- Given GT targets, one-pass stance quality lags two-pass systems (TC 69.74 vs 75.91; TG 64.13 vs 75.91, Table 4), since one-pass models cannot take target text as input.
Strengths
First unified one-pass architecture for both TSE scenarios
§3.2-3.3: a single BERTweet with two classifier heads for TC, and a single bart-base with an attention layer (mean decoder state as query over encoder states) for TG — replacing the two-model pipelines of Li et al. (2023) in both settings, which prior one-pass work (Akash et al., 2025, LLM-based, TG-only) did not do.
Actionable preprocessing finding with large effect sizes
Table 2: retaining target mentions lifts TC target-prediction F1 from 76.19 to 84.67 (two-pass) and rescues TG from a collapsed 25.96 to 49.32; Table 3 shows the same pattern for end-to-end TSE F1 (54.00→60.89 TC). Directly relevant to real deployments where mentions exist.
Concrete reproduction bug in a prior benchmark's preprocessing
Transparent, well-documented experimental setup
Precise footprint accounting
Weaknesses
No variance or significance testing despite seed averaging
Stance-quality gap is understated in the framing
TG comparison rests on an unverified conjecture
'Efficient' claim lacks runtime evidence
Evaluation depends on an unexamined mapping heuristic
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Scope & Claims
The paper targets Target-Stance Extraction (TSE) on the benchmark of Li et al. (2023): four English stance corpora (SemEval-2016 Task 6, an argument-mining corpus, COVID-19-Stance, P-Stance) plus Unrelated samples, covering 18 fixed targets (Table 1). Two claims are central: (1) a single model can replace the two-model pipeline in both benchmark scenarios, cutting trainable parameters by 49.89% (TC) and 48.32% (TG) at a modest F1 cost; (2) the community-standard practice of scrubbing target mentions from inputs systematically depresses measured accuracy, and removing it lifts F1 by at least 6 points. A third, valuable side-finding is that Li et al.'s #SemST-removal regex was neutralized by a casing bug (§4.2), meaning prior reported TC baselines carried a dataset-identifying hashtag cue.
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- Methods & statistical evidence assessment
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Frequently asked questions
What is Target-Stance Extraction (TSE)?
TSE is an NLP task introduced by Li et al. (2023): a system reads a document and must predict both the target (topic or entity) the author is writing about and the author's stance toward it (typically Favor, Against, or None), without being told the target in advance. Prior systems used two models in sequence; this paper predicts both with one model.
What is 'target scrubbing' and why does it matter?
Target scrubbing removes explicit mentions of the candidate targets from input texts before prediction, forcing models to rely on implicit cues. This paper shows the practice lowers measured accuracy by at least 6 F1 points, because real deployments contain those mentions and models can exploit them.
How much smaller is the one-pass model, and how much accuracy does it lose?
The one-pass models have about 48-50% fewer trainable parameters (absolute reductions of about 135.5M for Target Classification and 133.1M for Target Generation). With predicted targets, TSE F1 is 1.5-6.4 points below the two-pass systems; when gold targets are substituted, the stance-quality gap widens to roughly 6-12 points.
Is the code and data available for this paper?
The authors release code at github.com/elmines/multiling_tse. Experiments use the public TSE benchmark of Li et al. (2023) — SemEval-2016 Task 6, an argument-mining corpus, COVID-19-Stance, and P-Stance, plus Unrelated samples — and the KPTimes keyphrase dataset; the social-media texts themselves are not redistributed for privacy reasons.
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This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
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