Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification
This work addresses multi-label text classification, a common problem in NLP, but is incremental as it builds on existing set prediction and graph-based methods.
The paper tackles multi-label text classification by framing it as a set prediction task and using Graph Convolutional Networks to model label dependencies, achieving superior performance over baselines on two datasets.
Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation between labels, we leverage Graph Convolutional Networks and construct an adjacency matrix based on the statistical relations between labels. Additionally, we enhance recall ability by applying the Bhattacharyya distance to the output distributions of the set prediction networks. We evaluate the effectiveness of our approach on two multi-label datasets and demonstrate its superiority over previous baselines through experimental results.