# Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing

arXiv: https://arxiv.org/abs/2410.06190 · HTML: https://mlsanity.com/paper/2410.06190/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Improving Topic Models with Latent Feature Word Representations](https://mlsanity.com/paper/1810.06306/) (2018)
- [DAL: Dual Adversarial Learning for Dialogue Generation](https://mlsanity.com/paper/1906.09556/) (2019)
- [Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering](https://mlsanity.com/paper/1808.03733/) (2018)
- [InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries](https://mlsanity.com/paper/2409.19689/) (2024)
- [Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting](https://mlsanity.com/paper/2410.01724/) (2024)
- [Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue system](https://mlsanity.com/paper/1904.00785/) (2019)

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

- [Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues](https://mlsanity.com/paper/2412.09049/) (2024)
- [Proactive Dialogue Model with Intent Prediction](https://mlsanity.com/paper/2604.27379/) (2026)

## Abstract (arXiv)

With the growing importance of customer service in contemporary business, recognizing the intents behind service dialogues has become essential for the strategic success of enterprises. However, the nature of dialogue data varies significantly across different scenarios, and implementing an intent parser for a specific domain often involves tedious feature engineering and a heavy workload of data labeling. In this paper, we propose a novel Neural-Bayesian Program Learning model named Dialogue-Intent Parser (DI-Parser), which specializes in intent parsing under data-hungry settings and offers promising performance improvements. DI-Parser effectively utilizes data from multiple sources in a "Learning to Learn" manner and harnesses the "wisdom of the crowd" through few-shot learning capabilities on human-annotated datasets. Experimental results demonstrate that DI-Parser outperforms state-of-the-art deep learning models and offers practical advantages for industrial-scale applications.