Abstract:

As the core module of a task-oriented dialogue system, Natural Language Understanding (NLU) aims to structurally represent user inputs in natural language; this is generally decomposed into two subtasks: intent recognition and slot filling. Recently, the joint modeling of these two tasks has become a universal solution. However, establishing the connection between the two tasks is difficult to collect using a small number of support set samples in few-shot scenarios. Owing to domain gaps, the general knowledge learned from resource-rich source domains cannot be directly transferred to target domains. Inspired by cloze, this paper considers the average vector of non-slot (labeled as ″O″) words as the sentence pattern representation and proposes a Sentence Pattern Adaptive Prototype Network (SPAPN). In resource-rich source domains, the model fully learns the cross-domain semantic knowledge of sentence patterns and uses this information as a hub to indirectly model the relationship between intents and slots. Resource-low target domains adopt a meta-learning training mode and an attention mechanism to learn the correlation among the prototypes of intents, slots, and sentence patterns to enhance the semantic representations of intent and slot prototypes, and combine Comparative Alignment Learning (CAL) is employed to judge the labels of intents and slots based on the vector similarity between the query samples and these prototypes. Experiments conducted on Chinese and English benchmark datasets show that, irrespective of fine-tuning, the proposed method consistently outperforms state-of-the-art baselines in terms of intent accuracy, slot filling F1 score, and joint accuracy.

Key words: task-oriented dialogue system, intent recognition, slot filling, few-shot learning, attention mechanism

摘要：

作为任务型对话系统的核心模块, 自然语言理解(NLU)旨在将用户输入的自然语言进行结构化表示, 通常分为意图识别和槽位填充两个子任务。由于两者联系密切, 对意图和槽位进行显式联合建模成为通用的解决方案。然而, 在资源稀缺的小样本场景下较难通过少量支持集样本提取意图和槽位的关联关系, 且从资源丰富的源领域学习到的通用知识无法直接应用于目标领域。受英语完形填空任务启发, 将语句中非槽位(标签为"O")单词的平均向量视为句型表示, 提出一种句型自适应原型网络(SPAPN)方法。在资源丰富的源领域, 充分学习跨越领域的句型语义知识, 以句型信息为枢纽, 间接完成意图和槽位的关系建模。在低资源目标领域, 采用元学习的训练模式, 通过注意力机制学习意图、槽位、句型原型的关联关系, 获取意图和槽位的增强原型语义表示, 结合对比对齐学习(CAL)方法, 根据查询样本与原型之间的向量相似度判断其标签类别。在中英文基准数据集上的实验结果表明, 无论是否经过微调, 该方法较现有最优基线方法在意图识别准确率、槽位填充F1值以及联合准确率方面均能够取得更加优秀的表现。

关键词: 任务型对话系统, 意图识别, 槽位填充, 小样本学习, 注意力机制