Benchmarking Natural Language Understanding Services for Building Conversational Agents
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Building task-oriented dialogue agents generally requires two components: a Natural Language Understanding (NLU) and a Dialogue Management (DM) engine.
“Building task-oriented dialogue agents generally requires two components: a Natural Language Understanding (NLU) and a Dialogue Management (DM) engine (Bocklisch et al., 2017; Liu et al., 2021).”
From NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails (2023) · cites this paper as Liu et al., 2021
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Research works printed but not linked: 2
- Daniel Braun, Adrian Hernandez Mendez, Florian Matthes and Manfred Langen (2017) Eval- uating Natural Language Understanding Services for Conversational Question Answering Systems. In: Proceedings of SIGDIAL 2017,174-185.
- Massimo Canonico and Luigi De Russis (2018) A Comparison and Critique of Natural Lan- guage Understanding Tools. In: Proceedings of CLOUD COMPUTING 2018.
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Saka, A. B., Oyedele, L. O., Akanbi, L. A., Ganiyu, S. A., Chan, D. W. M., & Bello, S. A. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101869 · Cited by 161 · see citing works
NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails
Rebedea, T., Dinu, R., Sreedhar, M. N., Parisien, C., & Cohen, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2023.emnlp-demo.40 · Cited by 130 · see citing works
Chung, J., Kamar, E., & Amershi, S. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.34 · Cited by 72 · see citing works
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
Yen, H., Gao, T., Hou, M., Ding, K., Fleischer, D., Izsak, P., Wasserblat, M., & Chen, D. (2024) · arXiv · Cited by 2 · see citing works
“In-context learning (ICL) is a key ability that enables LLMs to adapt to new tasks on the fly (Brown et al., 2020 2024), we focus on datasets with large label spaces: TREC-course, TREC-fine (Li & Roth, 2002), BANKING77 (Casanueva et al., 2020), CLINC150 (Larson et al., 2019), and NLU (Liu et al., 2019).”
BackgroundEvaluating Memory in LLM Agents via Incremental Multi-Turn Interactions
Hu, Y., Wang, Y., & McAuley, J. (2025) · arXiv · doi:10.48550/arxiv.2507.05257 · Cited by 1 · see citing works
“Datasets for Test-Time Learning (TTL) We evaluate TTL via two task categories: (1) Multi-Class Classification (MCC) : We adopt five classification datasets used in prior TTL work [5, 50]: BANKING77 [6], CLINC150 [21], TREC-Coarse, TREC-Fine [26], and NLU [28].”
- Authors
- Liu, X., Eshghi, A., Swietojanski, P., & Rieser, V.
- Affiliations
- Heriot-Watt University (GB)
- Corresponding author
- Liu, Xingkun
- Venue
- Lecture Notes in Electrical Engineering
- Publisher
- Springer Singapore
- Published
- 2021
- Pages
- 165-183
- ISSN
- 1876-1100 (ISSN-L)
- 1876-1119
- OpenAlex
- W2966087730
- Cited by
- 208
- References
- 0
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 25 · 2023: 47 · 2022: 53 · 2021: 39 · 2020: 20 · 2019: 4
- FWCI
- 68.13
- Type
- book chapter
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in electrical engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- natural language understanding, dialogue act classification, intent classification, chatbots, IBM Watson, Dialogflow, Luis, rasa, annotated dataset
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