A pattern-aware self-attention network for distant supervised relation extraction

Autor: Heyan Huang, Yuming Shang, Xian-Ling Mao, Xin Sun, Wei Wei
Rok vydání: 2022
Předmět:
Zdroj: Information Sciences. 584:269-279
ISSN: 0020-0255
DOI: 10.1016/j.ins.2021.10.047
Popis: Distant supervised relation extraction is an efficient strategy of finding relational facts from unstructured text without labeled training data. A recent paradigm to develop relation extractors is using pre-trained Transformer language models to produce high-quality sentence representations. However, due to the original Transformer is weak at capturing local dependencies and phrasal structures, existing Transformer-based methods cannot identify various relational patterns in sentences. To address this issue, we propose a novel distant supervised relation extraction model, which employs a specific-designed pattern-aware self-attention network to automatically discover relational patterns for pre-trained Transformers in an end-to-end manner. Specifically, the proposed method assumes that the correlation between two adjacent tokens reflects the probability that they belong to the same pattern. Based on this assumption, a novel self-attention network is designed to generate the probability distribution of all patterns in a sentence. Then, the probability distribution is applied as a constraint in the first Transformer layer to encourage its attention heads to follow the relational pattern structures. As a result, fine-grained pattern information is enhanced in the pre-trained Transformer without losing global dependencies. Extensive experimental results on two popular benchmark datasets demonstrate that our model performs better than the state-of-the-art baselines.
Databáze: OpenAIRE
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