@inproceedings{lee-etal-2026-relational,
title = "Relational Grounding Failures in Zero-Shot Cross-Lingual Dialogue Summarization with Small Language Models",
author = "Lee, Yunwoo and
Li, Ziwa and
Huang, Jennifer",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.31/",
pages = "402--411",
ISBN = "979-8-89176-478-1",
abstract = "We diagnose relational grounding failures in zero-shot English-to-Chinese dialogue summarization with small language models (SLMs). We develop and apply a Dialogue-Semantic Grounding Error Taxonomy to evaluate three open-weight SLMs alongside a supervised mBART baseline. The taxonomy captures errors in entities, events, speaker roles, circumstances, modality, discourse outcomes, omissions, and language or format issues. Our analysis shows that SLM hallucinations often involve relation-sensitive distortions of dialogue-relevant content: models may preserve source-relevant participants, events, or circumstances while altering who did what, under what conditions, or with what outcome. These findings position the taxonomy as a diagnostic evaluation framework for dialogue summarization, revealing relation-sensitive faithfulness failures that reference-based metrics may obscure."
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<abstract>We diagnose relational grounding failures in zero-shot English-to-Chinese dialogue summarization with small language models (SLMs). We develop and apply a Dialogue-Semantic Grounding Error Taxonomy to evaluate three open-weight SLMs alongside a supervised mBART baseline. The taxonomy captures errors in entities, events, speaker roles, circumstances, modality, discourse outcomes, omissions, and language or format issues. Our analysis shows that SLM hallucinations often involve relation-sensitive distortions of dialogue-relevant content: models may preserve source-relevant participants, events, or circumstances while altering who did what, under what conditions, or with what outcome. These findings position the taxonomy as a diagnostic evaluation framework for dialogue summarization, revealing relation-sensitive faithfulness failures that reference-based metrics may obscure.</abstract>
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%0 Conference Proceedings
%T Relational Grounding Failures in Zero-Shot Cross-Lingual Dialogue Summarization with Small Language Models
%A Lee, Yunwoo
%A Li, Ziwa
%A Huang, Jennifer
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F lee-etal-2026-relational
%X We diagnose relational grounding failures in zero-shot English-to-Chinese dialogue summarization with small language models (SLMs). We develop and apply a Dialogue-Semantic Grounding Error Taxonomy to evaluate three open-weight SLMs alongside a supervised mBART baseline. The taxonomy captures errors in entities, events, speaker roles, circumstances, modality, discourse outcomes, omissions, and language or format issues. Our analysis shows that SLM hallucinations often involve relation-sensitive distortions of dialogue-relevant content: models may preserve source-relevant participants, events, or circumstances while altering who did what, under what conditions, or with what outcome. These findings position the taxonomy as a diagnostic evaluation framework for dialogue summarization, revealing relation-sensitive faithfulness failures that reference-based metrics may obscure.
%U https://aclanthology.org/2026.groundlm-1.31/
%P 402-411
Markdown (Informal)
[Relational Grounding Failures in Zero-Shot Cross-Lingual Dialogue Summarization with Small Language Models](https://aclanthology.org/2026.groundlm-1.31/) (Lee et al., GroundLM 2026)
ACL