BlendX: Complex Multi-Intent Detection with Blended Patterns

TL;DR

BlendX introduces more complex and diverse multi-intent detection datasets using rule-based heuristics and ChatGPT, revealing current models' limitations and prompting a reevaluation of MID approaches.

Contribution

The paper presents BlendX, a new suite of datasets with increased complexity and diversity for multi-intent detection, created with novel quality metrics and augmentation strategies.

Findings

State-of-the-art MID models perform poorly on BlendX datasets.

BlendX datasets exhibit greater diversity in intent patterns.

Current MID models need reexamination due to dataset challenges.

Abstract

Task-oriented dialogue (TOD) systems are commonly designed with the presumption that each utterance represents a single intent. However, this assumption may not accurately reflect real-world situations, where users frequently express multiple intents within a single utterance. While there is an emerging interest in multi-intent detection (MID), existing in-domain datasets such as MixATIS and MixSNIPS have limitations in their formulation. To address these issues, we present BlendX, a suite of refined datasets featuring more diverse patterns than their predecessors, elevating both its complexity and diversity. For dataset construction, we utilize both rule-based heuristics as well as a generative tool -- OpenAI's ChatGPT -- which is augmented with a similarity-driven strategy for utterance selection. To ensure the quality of the proposed datasets, we also introduce three novel metrics that assess the statistical properties of an utterance related to word count, conjunction use, and pronoun usage. Extensive experiments on BlendX reveal that state-of-the-art MID models struggle with the challenges posed by the new datasets, highlighting the need to reexamine the current state of the MID field. The dataset is available at https://github.com/HYU-NLP/BlendX.