基於指標網路的方法，用於多標籤多類別意圖的聯合提取和檢測。

A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents

October 29, 2024

作者: Ankan Mullick, Sombit Bose, Abhilash Nandy, Gajula Sai Chaitanya, Pawan Goyal

cs.AI

摘要

在任務導向對話系統中，意圖檢測對於解釋用戶查詢並提供適當回應至關重要。現有研究主要處理具有單一意圖的簡單查詢，缺乏處理具有多個意圖和提取不同意圖範圍的複雜查詢的有效系統。此外，多語言、多意圖數據集明顯缺乏。本研究解決三個關鍵任務：從查詢中提取多個意圖範圍、檢測多個意圖，以及開發多語言多標籤意圖數據集。我們引入了一個新穎的多標籤多類別意圖檢測數據集（MLMCID數據集），該數據集是從現有基準數據集中精心挑選而來。我們還提出了一種基於指針網絡的架構（MLMCID），用於提取意圖範圍並檢測具有粗略和細粒度標籤的多個意圖，以六元組的形式呈現。全面分析顯示，我們基於指針網絡的系統在各種數據集上的準確性和F1分數方面優於基準方法。

English

In task-oriented dialogue systems, intent detection is crucial for interpreting user queries and providing appropriate responses. Existing research primarily addresses simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents and extracting different intent spans. Additionally, there is a notable absence of multilingual, multi-intent datasets. This study addresses three critical tasks: extracting multiple intent spans from queries, detecting multiple intents, and developing a multi-lingual multi-label intent dataset. We introduce a novel multi-label multi-class intent detection dataset (MLMCID-dataset) curated from existing benchmark datasets. We also propose a pointer network-based architecture (MLMCID) to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets. Comprehensive analysis demonstrates the superiority of our pointer network-based system over baseline approaches in terms of accuracy and F1-score across various datasets.