Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
Source abstract
Abstract
Intent detection and slot filling tasks share common semantic features and are interdependent. The abundance of professional terminology in specific domains, which poses difficulties for entity recognition, subsequently impacts the performance of intent detection. To address this issue, this paper proposes a co-interactive model based on a knowledge graph (CIMKG) for intent detection and slot filling. The CIMKG model comprises three key components: (1) a knowledge graph-based shared encoder module that injects domain-specific expertise to enhance its semantic representation and solve the problem of entity recognition difficulties caused by professional terminology and then encodes short utterances; (2) a co-interactive module that explicitly establishes the relationship between intent detection and slot filling to address the inter-dependency of these processes; (3) two decoders that decode the intent detection and slot filling. The proposed CIMKG model has been validated using question–answer corpora from both the medical and architectural safety fields. The experimental results demonstrate that the proposed CIMKG model outperforms benchmark models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Web Data Mining and Analysis · Data Visualization and Analytics · Rough Sets and Fuzzy Logic
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China
School of Science, Shandong Jianzhu University, Jinan 250101, China
Funding
Major Scientific & Technological Projects of Shandong Province · National Natural Science Foundation of Shandong Province
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