Abstract
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking. Existing approaches generally fall short in tracking unknown slot values during inference and often have difficulties in adapting to new domains. In this paper, we propose a Transferable Dialogue State Generator (TRADE) that generates dialogue states from utterances using a copy mechanism, facilitating knowledge transfer when predicting (domain, slot, value) triplets not encountered during training. Our model is composed of an utterance encoder, a slot gate, and a state generator, which are shared across domains. Empirical results demonstrate that TRADE achieves state-of-the-art joint goal accuracy of 48.62% for the five domains of MultiWOZ, a human-human dialogue dataset. In addition, we show its transferring ability by simulating zero-shot and few-shot dialogue state tracking for unseen domains. TRADE achieves 60.58% joint goal accuracy in one of the zero-shot domains, and is able to adapt to few-shot cases without forgetting already trained domains.
Abstract (translated)
对领域本体的过度依赖和跨领域知识共享的缺乏是对话状态跟踪的两个现实而研究较少的问题。现有的方法在推理过程中往往无法跟踪未知的时隙值，并且在适应新的域时往往会遇到困难。在本文中，我们提出了一种可转换的对话状态生成器（trade），它使用复制机制从话语中生成对话状态，从而在预测训练中未遇到的（域、时隙、值）三元组时促进知识转移。我们的模型由一个发声编码器、一个时隙门和一个状态发生器组成，它们跨域共享。实证结果表明，对于人类对话数据集multiwoz的五个领域，贸易达到了最先进的联合目标精度48.62%。另外，通过模拟未知域的零镜头和少镜头对话状态跟踪，证明了其传输能力。trade在其中一个零炮领域达到了60.58%的联合目标准确率，并且能够在不忘记已经训练过的领域的情况下适应少数射击情况。
URL
https://arxiv.org/abs/1905.08743
