Abstract
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III ) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.- Anthology ID:
- 2020.emnlp-main.235
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2935–2943
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.235
- DOI:
- 10.18653/v1/2020.emnlp-main.235
- Cite (ACL):
- Jueqing Lu, Lan Du, Ming Liu, and Joanna Dipnall. 2020. Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2935–2943, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs (Lu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.235.pdf
- Code
- MemoriesJ/KAMG
- Data
- EURLEX57K, MIMIC-III