Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
By Haoyu Wang, Ming Tan, Mo Yu, Shiyu Chang, Dakuo Wang, Kun Xu, Xiaoxiao Guo, Saloni Potdar. 2019
104 citations recorded by the source corpus.
Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.
Subfields: Artificial Intelligence, Information Extraction
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Citations in the corpus
- Span-based Joint Entity and Relation Extraction with Transformer Pre-training (2019, methodology): “Also, Transformer models have been used for relation classification [33, 35]: The input text is fed once through a Transformer model and the resulting embeddings are classified.”
- Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling (2020, methodology): “Following previous work (Verga et al., 2018; Wang et al., 2019a), the document is encoded once by the encoder, and the classification of all entity pairs is based on the same contextual embedding.”