@inproceedings{toadoum-sari-etal-2026-detecting,
title = "Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in {F}rench Narratives",
author = "Toadoum Sari, Sakayo and
Robin, Nelly and
Auzanneau, Michelle and
Sais, Lakhdar and
Petit, V{\'e}ronique and
Veniard, Marie and
Jabbour, Said and
Delorme, Fabien",
editor = "Choi, Jinho D. and
Chen, Yun-Nung and
Funakoshi, Kotaro and
Emami, Ali",
booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2026",
address = "Atlanta, Georgia, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.sigdial-1.10/",
pages = "139--150",
abstract = "Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: police violence, smuggler exploitation, dangerous crossings, and family separation. We introduce the task of experiential intertextuality detection: automatically identifying shared experiential echoes across migration narratives without requiring annotated training data. From 108 French migration narratives spanning both corridors, we automatically generate sentence pairs and score them using annotation-free methods: lexical baselines, sentence embeddings, POS-based structural features, a migration-specific theme lexicon, context-aware narrative features, and zero-shot LLM scoring with Qwen2.5-7B and Mistral-7B under three prompting strategies. We validate all methods against 816 expertannotated intertextuality judgments (interannotator Krippendorff{'}s {\ensuremath{\alpha}}=0.27). Our results reveal that all surface, structural, and embedding methods correlate only weakly with expert judgments (r{\ensuremath{\leq}}0.30); Qwen2.5-7B zero-shot achieves the best single-method correlation (r=0.38); few-shot examples degrade Qwen but dramatically improve Mistral; narrative position significantly predicts intertextuality, with departure-phase pairs showing the highest experiential echoes; and a supervised hybrid combining all 31 features achieves r=0.45, a 21{\%} improvement over the best individual method."
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<abstract>Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: police violence, smuggler exploitation, dangerous crossings, and family separation. We introduce the task of experiential intertextuality detection: automatically identifying shared experiential echoes across migration narratives without requiring annotated training data. From 108 French migration narratives spanning both corridors, we automatically generate sentence pairs and score them using annotation-free methods: lexical baselines, sentence embeddings, POS-based structural features, a migration-specific theme lexicon, context-aware narrative features, and zero-shot LLM scoring with Qwen2.5-7B and Mistral-7B under three prompting strategies. We validate all methods against 816 expertannotated intertextuality judgments (interannotator Krippendorff’s \ensuremathα=0.27). Our results reveal that all surface, structural, and embedding methods correlate only weakly with expert judgments (r\ensuremathłeq0.30); Qwen2.5-7B zero-shot achieves the best single-method correlation (r=0.38); few-shot examples degrade Qwen but dramatically improve Mistral; narrative position significantly predicts intertextuality, with departure-phase pairs showing the highest experiential echoes; and a supervised hybrid combining all 31 features achieves r=0.45, a 21% improvement over the best individual method.</abstract>
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%0 Conference Proceedings
%T Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives
%A Toadoum Sari, Sakayo
%A Robin, Nelly
%A Auzanneau, Michelle
%A Sais, Lakhdar
%A Petit, Véronique
%A Veniard, Marie
%A Jabbour, Said
%A Delorme, Fabien
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F toadoum-sari-etal-2026-detecting
%X Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: police violence, smuggler exploitation, dangerous crossings, and family separation. We introduce the task of experiential intertextuality detection: automatically identifying shared experiential echoes across migration narratives without requiring annotated training data. From 108 French migration narratives spanning both corridors, we automatically generate sentence pairs and score them using annotation-free methods: lexical baselines, sentence embeddings, POS-based structural features, a migration-specific theme lexicon, context-aware narrative features, and zero-shot LLM scoring with Qwen2.5-7B and Mistral-7B under three prompting strategies. We validate all methods against 816 expertannotated intertextuality judgments (interannotator Krippendorff’s \ensuremathα=0.27). Our results reveal that all surface, structural, and embedding methods correlate only weakly with expert judgments (r\ensuremathłeq0.30); Qwen2.5-7B zero-shot achieves the best single-method correlation (r=0.38); few-shot examples degrade Qwen but dramatically improve Mistral; narrative position significantly predicts intertextuality, with departure-phase pairs showing the highest experiential echoes; and a supervised hybrid combining all 31 features achieves r=0.45, a 21% improvement over the best individual method.
%U https://aclanthology.org/2026.sigdial-1.10/
%P 139-150
Markdown (Informal)
[Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives](https://aclanthology.org/2026.sigdial-1.10/) (Toadoum Sari et al., SIGDIAL 2026)
ACL
- Sakayo Toadoum Sari, Nelly Robin, Michelle Auzanneau, Lakhdar Sais, Véronique Petit, Marie Veniard, Said Jabbour, and Fabien Delorme. 2026. Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 139–150, Atlanta, Georgia, USA. Association for Computational Linguistics.