Validate SSC across non-biomedical subject domains

Validate the effectiveness of sequential sentence classification and structural-similarity-based cross-lingual transfer in subject domains with rhetorical organizations different from medicine and the life sciences, such as the humanities and social sciences.

Background

The multilingual sequential sentence classification dataset consists primarily of structured abstracts from medicine and the life sciences, where explicit section headers and IMRaD-like rhetorical organization are common. The paper therefore leaves unresolved whether the reported transfer behavior and the usefulness of structural similarity generalize to fields with different discourse conventions.

References

Its effectiveness in subject domains with different rhetorical organizations, such as humanities and social sciences, remains to be validated.

— Improving Cross-Lingual Transfer for Sequential Sentence Classification in Research Papers via Structural Similarity  (2609.19650 - Yamauchi et al., 17 Sep 2026) in Section 'Limitations', subsection 'Subject domain bias'

The more relevant factor may be the prevalence of the IMRaD convention for structured abstracts rather than broad subject domain, and separating the two is left for future work.

— Improving Cross-Lingual Transfer for Sequential Sentence Classification in Research Papers via Structural Similarity  (2609.19650 - Yamauchi et al., 17 Sep 2026) in Section 'Limitations', subsection 'Subject domain bias'