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        <title>LIAAD INESCTEC at SemEval-2026 Task 4: Unsupervised Narrative Similarity via Discourse Representation Structures and Sentence Embeddings</title>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
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            <namePart type="given">Debanjan</namePart>
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            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
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    <abstract>In this paper, we describe an unsupervised approach using Discourse Representation Structures (DRS) for the SemEval-2026 Task 4. This task was Narrative Similarity and was formulated in two different tracks. Our team only developed a solution for track A, where the input is composed of a triplet: an anchor story, a story A, and a story B. The output in this formulation is to predict which story, A or B, is more similar to the anchor story. Our approach parsed each story and transformed in a DRS format,then we leveraged its structure and extracted features, performing ablation experiments inthe development dataset. Our strategy achieved 0.5975 accuracy in the official blind test set.</abstract>
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%0 Conference Proceedings
%T LIAAD INESCTEC at SemEval-2026 Task 4: Unsupervised Narrative Similarity via Discourse Representation Structures and Sentence Embeddings
%A Amorim, Evelin
%A Jorge, Alípio
%A Silvano, Purificação
%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 amorim-etal-2026-liaad
%X In this paper, we describe an unsupervised approach using Discourse Representation Structures (DRS) for the SemEval-2026 Task 4. This task was Narrative Similarity and was formulated in two different tracks. Our team only developed a solution for track A, where the input is composed of a triplet: an anchor story, a story A, and a story B. The output in this formulation is to predict which story, A or B, is more similar to the anchor story. Our approach parsed each story and transformed in a DRS format,then we leveraged its structure and extracted features, performing ablation experiments inthe development dataset. Our strategy achieved 0.5975 accuracy in the official blind test set.
%U https://aclanthology.org/2026.semeval-1.277/
%P 2193-2199

Markdown (Informal)

[LIAAD INESCTEC at SemEval-2026 Task 4: Unsupervised Narrative Similarity via Discourse Representation Structures and Sentence Embeddings](https://aclanthology.org/2026.semeval-1.277/) (Amorim et al., SemEval 2026)

• LIAAD INESCTEC at SemEval-2026 Task 4: Unsupervised Narrative Similarity via Discourse Representation Structures and Sentence Embeddings (Amorim et al., SemEval 2026)

ACL

• Evelin Amorim, Alípio Jorge, and Purificação Silvano. 2026. LIAAD INESCTEC at SemEval-2026 Task 4: Unsupervised Narrative Similarity via Discourse Representation Structures and Sentence Embeddings. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2193–2199, San Diego, California, USA. Association for Computational Linguistics.