Human-in-the-Loop Conversation Agent for Customer Service
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Escalation in customer service chatbots is a practical application of the human-in-the-loop concept for robust application of chatbots in consumer service provision.
“the notion of escalation in customer service chatbots is a practical application of the human-in-the-loop concept for robust application of chatbots in consumer service provision [83].”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [83]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.9883 · Cited by 1,639 · see citing works
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · see citing works
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Liu, B., & Lane, I. R. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1352 · Cited by 723 · see citing works
What Makes Users Trust a Chatbot for Customer Service? An Exploratory Interview Study
Følstad, A., Nordheim, C. B., & Bjørkli, C. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01437-7_16 · Cited by 360 · see citing works
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
Qin, L., Che, W., Li, Y., Wen, H., & Liu, T. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1214 · Cited by 302 · see citing works
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Wang, Y., Shen, Y., & Jin, H. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2050 · Cited by 211 · see citing works
Shah, P., Hakkani-Tür, D. Z., Liu, B., & Tür, G. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-3006 · Cited by 118 · see citing works
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Zhong, P., Wang, D., & Miao, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017492 · Cited by 83 · see citing works
Analysis of User Interaction with Service Oriented Chatbot Systems
Jenkins, M.-C., Churchill, R., Cox, S. J., & Smith, D. J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73110-8_9 · Cited by 66 · see citing works
Kucherbaev, P., Bozzon, A., & Houben, G. (2018) · IEEE Internet Computing · doi:10.1109/mic.2018.252095348 · Cited by 65 · see citing works
A Conversational Digital Assistant for Intelligent Process Automation
Rizk, Y., Isahagian, V., Boag, S., et al. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-58779-6_6 · Cited by 57 · see citing works
FastText-Based Intent Detection for Inflected Languages
Balodis, K., & Deksne, D. (2019) · Information · doi:10.3390/info10050161 · Cited by 23 · see citing works
NLP-PIPE: Latvian NLP Tool Pipeline
rs, Z. N. S. A. U., & Elita, C. I. R. (2018) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-912-6-183 · Cited by 13 · see citing works
A Unified Conversational Assistant Framework for Business Process AutomationPreprint
Rizk, Y., Bhandwalder, A., Boag, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.03543 · Cited by 11 · see citing works
Latvian Tweet Corpus and Investigation of Sentiment Analysis for Latvian
rcis, P. M. A. (2018) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-912-6-112 · Cited by 8 · see citing works
Bidirectional LSTM Tagger for Latvian Grammatical Error Detection
Deksne, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27947-9_5 · Cited by 7 · see citing works
Word Embeddings for Latvian Natural Language Processing Tools
rs, Z. N. S. A. U. (2016) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-701-6-167 · Cited by 4 · see citing works
Deep Neural Learning Approaches for Latvian Morphological Tagging
teris, P. P. E. (2016) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-701-6-160 · Cited by 4 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Research works printed but not linked: 1
- [15]Vasiljevs, A., Skadina, I., Deksne, D., Martins Kalis, T., Vira, L.: Application of virtual agents for delivery of information services. In: New Challenges of Economic and Business Development, pp. 702–713 (2017)
Cited by
Sorted by citations
A survey of human-in-the-loop for machine learning
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.014 · Cited by 800 · see citing works
Future directions for chatbot research: An interdisciplinary research agenda
Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247 · see citing works
Virtual agents and flow experience: an empirical examination of AI-powered chatbots
Baabdullah, A. M., Alalwan, A. A., Algharabat, R. S., Metri, B., & Rana, N. P. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121772 · Cited by 171 · see citing works
- Authors
- Paikens, P., Znotiņš, A., & Bārzdiņš, G.
- Affiliations
- UL Institute of Mathematics and Computer Science (LV)
- Corresponding author
- Paikens, Pēteris
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 277-284
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC7298186
- OpenAlex
- W3036071193
- Cited by
- 11
- References
- 20
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 3
- FWCI
- 0.84
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed Central (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- customer service automation, mobile operators, intent classification, slot filling, information extraction, rule-based processing, Latvian language, business requirements
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review