Improve Text Classification Accuracy with Intent Information
This work addresses a specific bottleneck in text classification for dialogue systems, but it appears incremental as it builds on existing methods by adding label information.
The paper tackled the problem of text classification in task-oriented dialogue systems by introducing label embeddings to incorporate label information, achieving remarkable performance on a benchmark dataset.
Text classification, a core component of task-oriented dialogue systems, attracts continuous research from both the research and industry community, and has resulted in tremendous progress. However, existing method does not consider the use of label information, which may weaken the performance of text classification systems in some token-aware scenarios. To address the problem, in this paper, we introduce the use of label information as label embedding for the task of text classification and achieve remarkable performance on benchmark dataset.