用上下文窗口提升对话意图识别准确率
Improved intent classification based on context information using a windows-based approach
- 基于卷积神经网络与BERT，通过滑动窗口融合历史对话
- 在巴西葡萄牙语数据集上，三组实验均显著优于无上下文基线
- 适合需要理解对话连贯性的智能客服系统开发者
对话系统中的自然语言理解模块包含意图分类任务，即判断用户话语的目标。以往方法仅依赖当前语句进行意图预测，未考虑对话流中前几轮对话（上下文）的作用。本文提出多种方法，研究上下文信息对意图分类的影响，通过将对话历史与当前语句拼接的方式实现融合。所提方法基于卷积神经网络，利用BERT获取有效向量表示，并采用窗口化策略进行意图分类。实验在由Wavy全球公司提供的真实巴西葡萄牙语对话数据集上进行，结果表明，在使用用户前一句及系统回复作为上下文的三种方案中，模型性能均显著优于仅使用孤立语句的基线模型。
原文摘要 · Abstract (English)
Conversational systems have a Natural Language Understanding (NLU) module. In this module, there is a task known as an intent classification that aims at identifying what a user is attempting to achieve from an utterance. Previous works use only the current utterance to predict the intent of a given query and they do not consider the role of the context (one or a few previous utterances) in the dialog flow for this task. In this work, we propose several approaches to investigate the role of contextual information for the intent classification task. Each approach is used to carry out a concatenation between the dialogue history and the current utterance. Our intent classification method is based on a convolutional neural network that obtains effective vector representations from BERT to perform accurate intent classification using an approach window-based. Our experiments were carried out on a real-world Brazilian Portuguese corpus with dialog flows provided by Wavy global company. Our results achieved substantial improvements over the baseline, isolated utterances (without context), in three approaches using the user's utterance and system's response from previous messages as dialogue context.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。