# Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities

arXiv: https://arxiv.org/abs/2001.00100 · HTML: https://mlsanity.com/paper/2001.00100/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 3 of 3)

- ["It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents](https://mlsanity.com/paper/2309.11653/) (2023)
- [Topic-switch adapted Japanese Dialogue System based on PLATO-2](https://mlsanity.com/paper/2302.11280/) (2023)
- [TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information](https://mlsanity.com/paper/2502.15714/) (2025)

## Abstract (arXiv)

Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals. Nevertheless, understanding user utterances with high accuracy remains a challenging task with these frameworks. Particularly, when building chatbots with large volume of domain-specific entities. In this paper, we describe the challenges and lessons learned from building a large scale virtual assistant for understanding and responding to equipment-related complaints. In the process, we describe an alternative scalable framework for: 1) extracting the knowledge about equipment components and their associated problem entities from short texts, and 2) learning to identify such entities in user utterances. We show through evaluation on a real dataset that the proposed framework, compared to off-the-shelf popular ones, scales better with large volume of entities being up to 30% more accurate, and is more effective in understanding user utterances with domain-specific entities.