Summary of Intent-aware Dialogue Generation and Multi-task Contrastive Learning For Multi-turn Intent Classification, by Junhua Liu and Yong Keat Tan and Bin Fu and Kwan Hui Lim
Intent-Aware Dialogue Generation and Multi-Task Contrastive Learning for Multi-Turn Intent Classification
by Junhua Liu, Yong Keat Tan, Bin Fu, Kwan Hui Lim
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the significant challenge of generating large-scale, domain-specific, multilingual multi-turn dialogue datasets for training effective Multi-Turn Intent Classification models in chatbot systems. To overcome this hurdle, the authors introduce Chain-of-Intent, a novel mechanism that combines Hidden Markov Models with Large Language Models (LLMs) to generate contextually aware, intent-driven conversations through self-play. The approach leverages LLMs to enhance emission probabilities and produces natural and contextually consistent questions and answers. Additionally, the paper proposes MINT-CL, a framework for multi-turn intent classification using multi-task contrastive learning, which improves classification accuracy without requiring extensive annotated data. The authors evaluate their methods and show that they outperform baselines in dialogue quality and intent classification accuracy, particularly in multilingual settings, while significantly reducing data generation efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make chatbots better by creating large amounts of conversation datasets for training. It introduces a new way to generate conversations using Hidden Markov Models and Large Language Models. This approach makes conversations more natural and contextually consistent. The authors also suggest a way to improve intent classification without needing lots of labeled data. They test their methods and show that they work well in different languages, which is important for chatbots that need to communicate with people who speak different languages. |
Keywords
» Artificial intelligence » Classification » Multi task
