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A New Direction in Stance Detection: Target-Stance Extraction in the Wild

Summary

Most stance detection systems assume the opinion's target is supplied with the text. On social media, however, targets are often only implied, and labeling them by hand at scale is impractical. The authors therefore define Target-Stance Extraction (TSE), which predicts both the target and the stance from raw text alone.

To benchmark TSE they build a two-stage pipeline:

  • Stage one identifies the target in one of two ways: classifying the text into a fixed target set, or generating keyphrases with BART fine-tuned on keyphrase corpora and mapping them onto the predefined list.
  • Stage two classifies stance toward the predicted target, with target prediction added as an auxiliary task in a multi-task setup.

Evaluation covers an in-target setting built by merging four stance corpora and a newly assembled zero-shot set of six unseen targets. Both include an extra Unrelated class.

The task proves difficult. In zero-shot TSE, stance scores with generated targets are roughly half those obtained with gold targets. The multi-task objective helps consistently. The contribution is mainly a more realistic problem formulation, and the code is publicly released.

Key points

  • In-target data: SemEval-2016, AM, COVID-19 (C19) and P-Stance are merged into 18 targets. Abortion targets are unified, and Climate Change is held out for zero-shot.
  • Zero-shot data: Each of six targets has 500 samples, for 3,000 test examples in total: the MeToo Movement, the Disney–Fox merger, the New York State lockdown, Creationism, Gay Rights, and the view that climate change is a concern. The Unrelated class is kept at a 5:1 ratio of targets of interest to Unrelated.
  • Target generation: BART is fine-tuned separately on OpenKP, KPTimes and FullTextKP using a One2Seq approach. KPTimes-based generation gave the best downstream stance results.
  • Zero-shot results: the best model, TGA-Net with KPTimes, reaches 22.83 F1, versus 40.94 F1 with ground-truth targets. The stance classifiers compared are BiLSTM, BiCond, TAN, CrossNet, TGA-Net and BERTweet.
  • Multi-task gains: the auxiliary target-prediction task raises average F (F_avg) by 2.35 on SemEval-2016, 0.80 on AM, 2.95 on COVID-19 and 0.92 on P-Stance. The largest gains appear on the smaller datasets. Code: github.com/chuchun8/TSE.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

15 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 13 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.acl-long.560

@inproceedings{li2023new,
  title = {{A New Direction in Stance Detection: Target-Stance Extraction in the Wild}},
  author = {Li, Yingjie and Garg, Krishna and Caragea, Cornelia},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.560},
  url = {https://doi.org/10.18653/v1/2023.acl-long.560}
}

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