Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion

Ipek Baris Schlicht, Lucie Flek, Paolo Rosso
1/13/2023
cs.CLcs.IR

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Abstract

Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages. (1) With a vast number of annotators available for world languages and the storage-efficient adapter models, this approach is more cost efficient. Models can be updated more frequently and thus stay up-to-date. (2) Adapter fusion provides insights and allows for interpretation regarding the influence of each adapter model on a particular language. The proposed solution often outperformed the top multilingual approaches in our benchmark tasks.

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Cite this paper

@article{schlicht2023multilingual,
  title  = {Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion},
  author = {Ipek Baris Schlicht and Lucie Flek and Paolo Rosso},
  year   = {2023},
  eprint = {2301.05494},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.05494v1}
}

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