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Aspect-Controlled Neural Argument Generation

Paper · 2021 · Benjamin Schiller, Johannes Daxenberger and Iryna Gurevych

Summary

This work addresses argument generation that users can steer precisely. Earlier neural generators tend to produce long passages and give little say over which angle an argument takes. The authors fine-tune the CTRL conditional language model into Arg-CTRL. Given a control code made of topic, stance (PRO/CON) and aspect, it outputs one concise argumentative sentence. Training data come from millions of documents on eight controversial topics, drawn from Common-Crawl and Reddit-Comments. An existing model labels each sentence's stance, and a new aspect detector assigns aspects. The detector is trained on a crowdsourced dataset built on the UKP-Corpus. Because free span marking proved unreliable, non-expert annotators pick from ranked aspect suggestions produced by a BERT/MT-DNN ranker. Evaluation shows the model produces genuine, grammatical, aspect-specific arguments. Training on Common-Crawl gives better results than training on Reddit. Reversing the stance in the control code yields interpretable counter-arguments. The approach could support debate preparation, essay writing and more balanced discussions. Model weights, data and code are publicly released.

Key points

  • New aspect detection dataset: 5,032 arguments with aspect annotations, derived from the UKP Sentential Argument Mining Corpus (25,474 sentences, eight topics).
  • A pilot study had two experts annotate 800 sentences with free token spans and reached only Krippendorff's α_u = .38. This led to a ranked-recommendation scheme, with candidates limited to 1–4 tokens (96% of pilot aspects fit this length).
  • When training lacks explicit aspect conditioning, 92% of the generated sentences do not address the requested aspect. This shows that aspect detection is a necessary step.
  • Arg-CTRL cannot handle topics unseen in training. Training uses balanced pro/con classes, and the data is released for research use only, under access control.
  • Code and resources are available at github.com/UKPLab/controlled-argument-generation.

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

 Institutions

🎓Technische Universität DarmstadtDarmstadt · Germany76 works · 35,008 citations

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksPhilosophy · 211 works

Cite

Cite as Benjamin Schiller, Johannes Daxenberger and Iryna Gurevych. 2021. Aspect-Controlled Neural Argument Generation. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.naacl-main.34

@inproceedings{schiller2021aspect,
  title = {{Aspect-Controlled Neural Argument Generation}},
  author = {Benjamin Schiller, Johannes Daxenberger and Iryna Gurevych},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.34},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.34}
}

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