用少样本学习提升对话意图解析，减少人工标注
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing
- 融合神经网络与贝叶斯程序学习，实现跨领域少样本解析
- 在少量标注数据下仍保持高准确率，优于现有深度模型
- 适合需要快速部署的工业级对话系统应用
随着企业客户服务重要性提升，识别对话中的用户意图成为关键。然而，不同场景下的对话数据差异大，为特定领域构建意图解析器需大量特征工程和人工标注。本文提出一种名为DI-Parser的神经-贝叶斯程序学习模型，在数据稀缺条件下表现出色。该模型通过‘学习如何学习’的方式整合多源数据，并利用人类标注数据集的‘群体智慧’实现少样本学习。实验表明，DI-Parser显著优于当前先进深度学习模型，具备工业级应用的实用优势。
原文摘要 · Abstract (English)
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.
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