@inproceedings{kowalski-etal-2026-exploring,
title = "Exploring Retrieval Augmented Generation Approaches for Natural Language Question Understanding",
author = "Kowalski, Christoph and
Robrecht-Hilbig, Amelie Sophie and
Emmerling, Vincent and
Kopp, Stefan",
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-1.34/",
pages = "486--496",
abstract = "Language-based interactive AI systems are required to provide adaptive explanations in order to create transparency and interpretability. A cornerstone ability for this is to respond to user questions. We target the problem of Natural Language Question Understanding (NLQU), which refers to interpreting a user question and mapping it to semantic representations (items in a structured knowledge base) that are relevant to address the underlying user{'}s knowledge gap and hence to answer the question. In contrast to pure Q{\&}A systems that aim to directly map questions to answers, NLQU enables a dialog agent to employ different explanation strategies, including explaining prerequisite knowledge, adapting explanation speed, or identifying and repairing misunderstandings. This paper explores the use of modern Large Language Models (LLMs) along with Retrieval Augmented Generation (RAG) for NLQU. We present a RAG-based NLQU component, evaluate different approaches against a synthetic dataset, and test the final component on a natural language question dataset from a human-human explanation study. Different LLM models, prompts, and hyperparameters are tested and compared to a baseline method."
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<abstract>Language-based interactive AI systems are required to provide adaptive explanations in order to create transparency and interpretability. A cornerstone ability for this is to respond to user questions. We target the problem of Natural Language Question Understanding (NLQU), which refers to interpreting a user question and mapping it to semantic representations (items in a structured knowledge base) that are relevant to address the underlying user’s knowledge gap and hence to answer the question. In contrast to pure Q&A systems that aim to directly map questions to answers, NLQU enables a dialog agent to employ different explanation strategies, including explaining prerequisite knowledge, adapting explanation speed, or identifying and repairing misunderstandings. This paper explores the use of modern Large Language Models (LLMs) along with Retrieval Augmented Generation (RAG) for NLQU. We present a RAG-based NLQU component, evaluate different approaches against a synthetic dataset, and test the final component on a natural language question dataset from a human-human explanation study. Different LLM models, prompts, and hyperparameters are tested and compared to a baseline method.</abstract>
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%0 Conference Proceedings
%T Exploring Retrieval Augmented Generation Approaches for Natural Language Question Understanding
%A Kowalski, Christoph
%A Robrecht-Hilbig, Amelie Sophie
%A Emmerling, Vincent
%A Kopp, Stefan
%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 kowalski-etal-2026-exploring
%X Language-based interactive AI systems are required to provide adaptive explanations in order to create transparency and interpretability. A cornerstone ability for this is to respond to user questions. We target the problem of Natural Language Question Understanding (NLQU), which refers to interpreting a user question and mapping it to semantic representations (items in a structured knowledge base) that are relevant to address the underlying user’s knowledge gap and hence to answer the question. In contrast to pure Q&A systems that aim to directly map questions to answers, NLQU enables a dialog agent to employ different explanation strategies, including explaining prerequisite knowledge, adapting explanation speed, or identifying and repairing misunderstandings. This paper explores the use of modern Large Language Models (LLMs) along with Retrieval Augmented Generation (RAG) for NLQU. We present a RAG-based NLQU component, evaluate different approaches against a synthetic dataset, and test the final component on a natural language question dataset from a human-human explanation study. Different LLM models, prompts, and hyperparameters are tested and compared to a baseline method.
%U https://aclanthology.org/2026.sigdial-1.34/
%P 486-496
Markdown (Informal)
[Exploring Retrieval Augmented Generation Approaches for Natural Language Question Understanding](https://aclanthology.org/2026.sigdial-1.34/) (Kowalski et al., SIGDIAL 2026)
ACL