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Cross-Domain Label-Adaptive Stance Detection

Summary

Stance detection, which identifies the position a text takes toward some target, comes in many incompatible forms. Inputs can be tweets or full news articles, and targets can be claims or explicit or implicit topics. The biggest obstacle is that label sets differ: besides agree/disagree/unrelated, datasets use labels such as discuss, question or comment. This makes it hard to pool data or transfer models across domains. The authors assemble 16 English stance datasets (about 253K examples) from debates, news, social media and mixed sources and analyse their properties. They propose MoLE, an end-to-end framework on top of pre-trained Transformers. It combines domain adaptation (mixture of experts plus domain-adversarial training) with label embeddings so that heterogeneous label inventories can be learned jointly. For unseen domains, MoLE maps its outputs to user-defined labels without supervision, based on how similar the label names are. MoLE outperforms strong baselines on both seen (in-domain) and unseen (out-of-domain) targets. The work provides a unified testbed and method for cross-domain stance detection, which is useful for fact-checking and content analysis.

Key points

  • Datasets: 16 datasets, including fnc1, emergent, snopes, semeval2016t6, semeval2019t7, wtwt, vast, perspectrum, arc and ibmcs, grouped into Debates, News, Social Media and Various.
    • In total there are 253,270 examples (154,228 train / 30,547 dev / 68,495 test).
    • Some tweets in mtsd and rumor could no longer be downloaded.
  • Method: MoLE merges mixture of experts, domain-adversarial training and label embeddings. The best out-of-domain variant, "MoLE w/ weak mappings", relies on similarity between label names.
  • Correlation analysis: The authors computed Pearson correlations between dataset features and out-of-domain F1.
    • Larger train and dev sets help, while larger test sets and larger vocabularies hurt.
    • Surprisingly, target overlap with the training data lowers F1.
    • News, Headline targets, article bodies and claims as context correlate positively with F1, whereas tweets as context correlate negatively.
  • Transfer factors: The authors name the source domain, vocabulary size and number of unique labels as the key factors for successful transfer.
  • Release: Code, models and data are available at github.com/checkstep/mole-stance.

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

40 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 33 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.emnlp-main.710 · arXiv 2104.07467

@inproceedings{hardalov2021cross,
  title = {{Cross-Domain Label-Adaptive Stance Detection}},
  author = {Hardalov, Momchil and Arora, Arnav and Nakov, Preslav and Augenstein, Isabelle},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  year = {2021},
  doi = {10.18653/v1/2021.emnlp-main.710},
  eprint = {2104.07467},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.emnlp-main.710}
}

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