Convolutional Neural Networks for Dialogue State Tracking without Pre-Trained Word Vectors or Semantic Dictionaries

A crucial step in task-oriented dialogue systems is tracking the user’s goal over the course of the conversation. This involves maintaining a probability distribution over possible values for each slot (e.g., the foodslot might map to the value Turkish), which gets updated at each turn of the dialogue. Previously, rule-based methods were applied to dialogue systems, or models that required hand-crafted semantic dictionaries mapping phrases to those that are similar in meaning (e.g., areamight map to part of town). However, these are expensive to design for each domain, limiting the generalizability. In addition, often a spoken language understanding (SLU) component precedes the dialogue state update mechanism; however, this leads to compounded errors as the output from one module is passed to the next. Instead, more recent work has explored deep learning models for directly updating dialogue state, bypassing the need for SLU or expert-engineered rules. We demonstrate that a novel convolutional neural architecture without any pre-trained word vectors or semantic dictionaries achieves 86.9% joint goal accuracy and 95.4% requested slot accuracy on WOZ 2.0.