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    <abstract>This paper presents Team HausaNLP’s submission to SemEval-2026 Task 4 (Track A),which requires identifying the more narrativelysimilar of two candidate stories relative to ananchor. Narrative similarity is defined alongthree dimensions: abstract theme, course ofaction, and story outcomes. We conduct a systematic ablation comparing five approaches:a lexical TF-IDF baseline, two bi-encoderSBERT variants (all-MiniLM-L6-v2 andall-mpnet-base-v2), a paraphrase-focusedembedding model, and a cross-encoder reranker. On the 200-instance development set,all-mpnet-base-v2 achieves the best performance (61.5% accuracy, 61.48 macro-F1), outperforming both TF-IDF (54.5%) and the official SBERT baseline (55.0%). Surprisingly,the cross-encoder re-ranker (55.5%) does notimprove on the bi-encoders, which we attributeto the long-document nature of Wikipedia storysummaries exceeding the model’s effective context window. On the official test set, our primary SBERT MiniLM submission achieved61.50% accuracy (33rd of 44 teams). Our erroranalysis over 200 development instances identifies five systematic failure categories, distinctfrom the All Correct / Partial cases, including23 Lexical Trap cases, 23 Hard Cases, and 24Proposed-Recovery cases, thereby informingconcrete directions for future work.</abstract>
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%0 Conference Proceedings
%T Team HausaNLP at SemEval-2026 Task 4: Narratives via Semantic Embeddings
%A Adam, Faisal
%A Aliyu, Lukman
%A Aji, Sani
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F adam-etal-2026-team
%X This paper presents Team HausaNLP’s submission to SemEval-2026 Task 4 (Track A),which requires identifying the more narrativelysimilar of two candidate stories relative to ananchor. Narrative similarity is defined alongthree dimensions: abstract theme, course ofaction, and story outcomes. We conduct a systematic ablation comparing five approaches:a lexical TF-IDF baseline, two bi-encoderSBERT variants (all-MiniLM-L6-v2 andall-mpnet-base-v2), a paraphrase-focusedembedding model, and a cross-encoder reranker. On the 200-instance development set,all-mpnet-base-v2 achieves the best performance (61.5% accuracy, 61.48 macro-F1), outperforming both TF-IDF (54.5%) and the official SBERT baseline (55.0%). Surprisingly,the cross-encoder re-ranker (55.5%) does notimprove on the bi-encoders, which we attributeto the long-document nature of Wikipedia storysummaries exceeding the model’s effective context window. On the official test set, our primary SBERT MiniLM submission achieved61.50% accuracy (33rd of 44 teams). Our erroranalysis over 200 development instances identifies five systematic failure categories, distinctfrom the All Correct / Partial cases, including23 Lexical Trap cases, 23 Hard Cases, and 24Proposed-Recovery cases, thereby informingconcrete directions for future work.
%U https://aclanthology.org/2026.semeval-1.7/
%P 48-53

Markdown (Informal)

[Team HausaNLP at SemEval-2026 Task 4: Narratives via Semantic Embeddings](https://aclanthology.org/2026.semeval-1.7/) (Adam et al., SemEval 2026)

• Team HausaNLP at SemEval-2026 Task 4: Narratives via Semantic Embeddings (Adam et al., SemEval 2026)

ACL

• Faisal Adam, Lukman Aliyu, and Sani Aji. 2026. Team HausaNLP at SemEval-2026 Task 4: Narratives via Semantic Embeddings. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 48–53, San Diego, California, USA. Association for Computational Linguistics.