@inproceedings{li-etal-2026-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 8: Parallel Generation and Multi-Metric Reranking for Faithful Extractive {RAG}",
author = "Li, Bo and
Zhang, You and
Wang, Jin and
Xu, Dan and
Zhang, Xuejie",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.130/",
pages = "943--949",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents our approach for the SemEval-2026 Task 8: MTRAGEval (SubtaskB: Answer Generation), which challenges sys-tems to generate faithful, extractive answers to multi-turn questions based strictly on provided gold-standard reference passages. The primary scientific challenge lies in maintaining high faithfulness and structural consistency while adapting to diverse answer styles across a conversation, as systems must generate responses that vary significantly in length and format without hallucinating. Conventional reference-based generation methods often rely on static prompting or greedy decoding, which fail to capture these dynamic stylistic requirements and lack robustness against generation noise. To address these limitations, we propose a Intent-Aware Parallel Generation and Reranking System powered by a large language model. Experimental results on the official test set demonstrate the effectiveness of our method, achieving competitive performance comparable to SoTA baselines. Ultimately,our approach secured the third place in the competition. The code of the paper is available at: https://github.com/viaviachris/SemEval-2026-Task8"
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<abstract>This paper presents our approach for the SemEval-2026 Task 8: MTRAGEval (SubtaskB: Answer Generation), which challenges sys-tems to generate faithful, extractive answers to multi-turn questions based strictly on provided gold-standard reference passages. The primary scientific challenge lies in maintaining high faithfulness and structural consistency while adapting to diverse answer styles across a conversation, as systems must generate responses that vary significantly in length and format without hallucinating. Conventional reference-based generation methods often rely on static prompting or greedy decoding, which fail to capture these dynamic stylistic requirements and lack robustness against generation noise. To address these limitations, we propose a Intent-Aware Parallel Generation and Reranking System powered by a large language model. Experimental results on the official test set demonstrate the effectiveness of our method, achieving competitive performance comparable to SoTA baselines. Ultimately,our approach secured the third place in the competition. The code of the paper is available at: https://github.com/viaviachris/SemEval-2026-Task8</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2026 Task 8: Parallel Generation and Multi-Metric Reranking for Faithful Extractive RAG
%A Li, Bo
%A Zhang, You
%A Wang, Jin
%A Xu, Dan
%A Zhang, Xuejie
%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 li-etal-2026-ynu
%X This paper presents our approach for the SemEval-2026 Task 8: MTRAGEval (SubtaskB: Answer Generation), which challenges sys-tems to generate faithful, extractive answers to multi-turn questions based strictly on provided gold-standard reference passages. The primary scientific challenge lies in maintaining high faithfulness and structural consistency while adapting to diverse answer styles across a conversation, as systems must generate responses that vary significantly in length and format without hallucinating. Conventional reference-based generation methods often rely on static prompting or greedy decoding, which fail to capture these dynamic stylistic requirements and lack robustness against generation noise. To address these limitations, we propose a Intent-Aware Parallel Generation and Reranking System powered by a large language model. Experimental results on the official test set demonstrate the effectiveness of our method, achieving competitive performance comparable to SoTA baselines. Ultimately,our approach secured the third place in the competition. The code of the paper is available at: https://github.com/viaviachris/SemEval-2026-Task8
%U https://aclanthology.org/2026.semeval-1.130/
%P 943-949
Markdown (Informal)
[YNU-HPCC at SemEval-2026 Task 8: Parallel Generation and Multi-Metric Reranking for Faithful Extractive RAG](https://aclanthology.org/2026.semeval-1.130/) (Li et al., SemEval 2026)
ACL