Open intent detection, a crucial aspect of natural language understanding,
involves the identification of previously unseen intents in user-generated
text. Despite the progress made in this field, challenges persist in handling
new combinations of language components, which is essential for compositional
generalization. In this paper, we present a case study exploring the use of
ChatGPT as a data augmentation technique to enhance compositional
generalization in open intent detection tasks. We begin by discussing the
limitations of existing benchmarks in evaluating this problem, highlighting the
need for constructing datasets for addressing compositional generalization in
open intent detection tasks. By incorporating synthetic data generated by
ChatGPT into the training process, we demonstrate that our approach can
effectively improve model performance. Rigorous evaluation of multiple
benchmarks reveals that our method outperforms existing techniques and
significantly enhances open intent detection capabilities. Our findings
underscore the potential of large language models like ChatGPT for data
augmentation in natural language understanding tasks.

本文通过案例研究，以开放式意图检测任务为研究对象，探讨使用 ChatGPT 作为数据增强技术以提高组合泛化性能，通过构建数据集并整合 ChatGPT 生成的合成数据来训练模型，通过严格评估多个基准测试，发现我们的方法在开放式意图检测中表现优于现有技术，并显著提升了模型性能，从而突显出 ChatGPT 等大型语言模型在自然语言理解任务中作为数据增强方法的潜力。

ChatGPT 作为数据增强在组合泛化中的应用研究：以开放意图检测为例

ChatGPT as Data Augmentation for Compositional Generalization: A Case  Study in Open Intent Detection

Open intent detection is a significant problem in natural language
understanding, which aims to detect the unseen open intent with the prior
knowledge of only known intents. Current methods have two core challenges in
this task. On the one hand, they have limitations in learning friendly
representations to detect the open intent. On the other hand, there lacks an
effective approach to obtaining specific and compact decision boundaries for
known intents. To address these issues, this paper introduces an original
framework, DA-ADB, which successively learns distance-aware intent
representations and adaptive decision boundaries for open intent detection.
Specifically, we first leverage distance information to enhance the
distinguishing capability of the intent representations. Then, we design a
novel loss function to obtain appropriate decision boundaries by balancing both
empirical and open space risks. Extensive experiments show the effectiveness of
distance-aware and boundary learning strategies. Compared with the
state-of-the-art methods, our method achieves substantial improvements on three
benchmark datasets. It also yields robust performance with different
proportions of labeled data and known categories. The full data and codes are
available at this https URL

本文提出了一个名为 DA-ADB 的框架，通过距离感知意图表示和自适应决策边界的学习，解决了开放意图检测中的两个核心难题，实验结果表明，该方法相对于现有技术在各项基准测试数据集上表现得到了显著的提升。

开放式意图检测中的判别表示和决策边界学习

Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

TEXTOIR is the first integrated and visualized platform for text open intent
recognition. It is composed of two main modules: open intent detection and open
intent discovery. Each module integrates most of the state-of-the-art
algorithms and benchmark intent datasets. It also contains an overall framework
connecting the two modules in a pipeline scheme. In addition, this platform has
visualized tools for data and model management, training, evaluation and
analysis of the performance from different aspects. TEXTOIR provides useful
toolkits and convenient visualized interfaces for each sub-module (Toolkit
code: this https URL), and designs a framework to implement
a complete process to both identify known intents and discover open intents
(Demo code: this https URL).

TEXTOIR 是一种视觉化的文本意图识别平台，由开放意图检测和开放意图发现两个主要模块组成，并包含数据和模型管理、培训、评估和分析的可视化工具。