@inproceedings{elchafei-etal-2026-h,
title = "{H}-{RAG} at {S}em{E}val-2026 Task 8: Hierarchical Parent{--}Child Retrieval for Multi-Turn {RAG} Conversations",
author = "Elchafei, Passant and
Emam, Hossam and
Alansary, Mohamed and
Swain, Monorama and
Schedl, Markus",
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-1.155/",
pages = "1136--1143",
ISBN = "979-8-89176-414-9",
abstract = "We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages). Task A evaluates standalone retrieval quality, while Task C assesses end-to-end retrieval-augmented generation (RAG) in multi-turn conversational settings, requiring both accurate answer generation and faithful grounding in retrieved evidence. Our approach implements a hierarchical parent{--}child RAG pipeline that separates fine-grained child-level retrieval from parent-level context reconstruction during generation. Documents are segmented into overlapping sentence-based child chunks, while full documents are preserved as parent units to provide coherent context. weighting, and embedding-based similarity rescoring over child chunks. Retrieved evidence is aggregated at the parent level and supplied to an instruction-tuned language model for response generation. H-RAG achieves an nDCG@5 score of 0.4271 on Task A and a harmonic mean score of 0.3241 on Task C (RBagg: 0.2488, RLF: 0.2703, RBllm: 0.6508), underscoring the importance of retrieval configuration and parent-level aggregation in multi-turn RAG performance."
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<abstract>We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages). Task A evaluates standalone retrieval quality, while Task C assesses end-to-end retrieval-augmented generation (RAG) in multi-turn conversational settings, requiring both accurate answer generation and faithful grounding in retrieved evidence. Our approach implements a hierarchical parent–child RAG pipeline that separates fine-grained child-level retrieval from parent-level context reconstruction during generation. Documents are segmented into overlapping sentence-based child chunks, while full documents are preserved as parent units to provide coherent context. weighting, and embedding-based similarity rescoring over child chunks. Retrieved evidence is aggregated at the parent level and supplied to an instruction-tuned language model for response generation. H-RAG achieves an nDCG@5 score of 0.4271 on Task A and a harmonic mean score of 0.3241 on Task C (RBagg: 0.2488, RLF: 0.2703, RBllm: 0.6508), underscoring the importance of retrieval configuration and parent-level aggregation in multi-turn RAG performance.</abstract>
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%0 Conference Proceedings
%T H-RAG at SemEval-2026 Task 8: Hierarchical Parent–Child Retrieval for Multi-Turn RAG Conversations
%A Elchafei, Passant
%A Emam, Hossam
%A Alansary, Mohamed
%A Swain, Monorama
%A Schedl, Markus
%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 elchafei-etal-2026-h
%X We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages). Task A evaluates standalone retrieval quality, while Task C assesses end-to-end retrieval-augmented generation (RAG) in multi-turn conversational settings, requiring both accurate answer generation and faithful grounding in retrieved evidence. Our approach implements a hierarchical parent–child RAG pipeline that separates fine-grained child-level retrieval from parent-level context reconstruction during generation. Documents are segmented into overlapping sentence-based child chunks, while full documents are preserved as parent units to provide coherent context. weighting, and embedding-based similarity rescoring over child chunks. Retrieved evidence is aggregated at the parent level and supplied to an instruction-tuned language model for response generation. H-RAG achieves an nDCG@5 score of 0.4271 on Task A and a harmonic mean score of 0.3241 on Task C (RBagg: 0.2488, RLF: 0.2703, RBllm: 0.6508), underscoring the importance of retrieval configuration and parent-level aggregation in multi-turn RAG performance.
%U https://aclanthology.org/2026.semeval-1.155/
%P 1136-1143
Markdown (Informal)
[H-RAG at SemEval-2026 Task 8: Hierarchical Parent–Child Retrieval for Multi-Turn RAG Conversations](https://aclanthology.org/2026.semeval-1.155/) (Elchafei et al., SemEval 2026)
ACL