Summary of Neural-bayesian Program Learning For Few-shot Dialogue Intent Parsing, by Mengze Hong et al.
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing
by Mengze Hong, Di Jiang, Yuanfeng Song, Chen Jason Zhang
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Neural-Bayesian Program Learning model, Dialogue-Intent Parser (DI-Parser), is proposed to recognize the intents behind service dialogues in contemporary business. This model specializes in intent parsing under data-hungry settings and achieves promising performance improvements. DI-Parser utilizes data from multiple sources through “Learning to Learn” and harnesses the “wisdom of the crowd” via few-shot learning on human-annotated datasets, outperforming state-of-the-art deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Dialogue-Intent Parser (DI-Parser) is a new model that helps businesses understand what customers want when they talk. It works by using information from many different places and getting smarter as it goes along. This makes it really good at figuring out what people mean, even with only a little bit of training data. The model is better than other deep learning models and can be used in real-world business settings. |
Keywords
» Artificial intelligence » Deep learning » Few shot » Parsing
