# One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots

arXiv: https://arxiv.org/abs/2112.08261 · HTML: https://mlsanity.com/paper/2112.08261/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://mlsanity.com/paper/1903.05566/) (2019)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [A Survey of Word Embeddings Evaluation Methods](https://mlsanity.com/paper/1801.09536/) (2018)
- [Word2Vec applied to Recommendation: Hyperparameters Matter](https://mlsanity.com/paper/1804.04212/) (2018)
- [DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder](https://mlsanity.com/paper/2109.10480/) (2021)
- [Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models](https://mlsanity.com/paper/2011.03259/) (2020)
- [Do We Need Online NLU Tools?](https://mlsanity.com/paper/2011.09825/) (2020)
- [Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning](https://mlsanity.com/paper/2107.02759/) (2021)
- [Fake News Detection in Spanish Using Deep Learning Techniques](https://mlsanity.com/paper/2110.06461/) (2021)
- [Joint model for intent and entity recognition](https://mlsanity.com/paper/2109.03221/) (2021)

## Built on by (most cited first, 2 of 2)

- [Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation](https://mlsanity.com/paper/2604.11077/) (2026)
- [Learning When to Automate: Queue Control in Human-AI Service Systems](https://mlsanity.com/paper/2607.06017/) (2026)

## Abstract (arXiv)

Customer service chatbots are conversational systems designed to provide information to customers about products/services offered by different companies. Particularly, intent recognition is one of the core components in the natural language understating capabilities of a chatbot system. Among the different intents that a chatbot is trained to recognize, there is a set of them that is universal to any customer service chatbot. Universal intents may include salutation, switch the conversation to a human agent, farewells, among others. A system to recognize those universal intents will be very helpful to optimize the training process of specific customer service chatbots. We propose the development of a universal intent recognition system, which is trained to recognize a selected group of 11 intents that are common in 28 different chatbots. The proposed system is trained considering state-of-the-art word-embedding models such as word2vec and BERT, and deep classifiers based on convolutional and recurrent neural networks. The proposed model is able to discriminate between those universal intents with a balanced accuracy up to 80.4%. In addition, the proposed system is equally accurate to recognize intents expressed both in short and long text requests. At the same time, misclassification errors often occurs between intents with very similar semantic fields such as farewells and positive comments. The proposed system will be very helpful to optimize the training process of a customer service chatbot because some of the intents will be already available and detected by our system. At the same time, the proposed approach will be a suitable base model to train more specific chatbots by applying transfer learning strategies.