mlsanityData to 14 Oct 2026

Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support

arXiv 2302.03222 ↗First posted 7 Feb 2023cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at urlhttps://github.com/VectorInstitute/NAA

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 7 Feb 2023.

Builds on

17 of 23 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  6. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  7. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  8. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  9. 2019Passage Re-ranking with BERTcited by 645
  10. 2019ELI5: Long Form Question Answeringcited by 575
  11. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 433
  12. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 386
5 more
  1. 2021CUAD: An Expert-Annotated NLP Dataset for Legal Contract Reviewcited by 236
  2. 2021Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Modelscited by 141
  3. 2021COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted Listcited by 113
  4. 2021Effectiveness of Pre-training for Few-shot Intent Classificationcited by 27
  5. 2022Multimodal Conversational AI: A Survey of Datasets and Approachescited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agentscited by 22
  2. 2024FAIIR: Building Toward A Conversational AI Agent Assistant for Youth Mental Health Service Provisioncited by 3
  3. 2026Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Frameworkcited by 3
  4. 2025Unveiling Dual Quality in Product Reviews: An NLP-Based Approachcited by 0
  5. 2025textitGrahak-Nyay: Consumer Grievance Redressal through Large Language Modelscited by 0
  6. 2026Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflowscited by 0