Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction

• URL: http://arxiv.org/abs/2003.11518v2
• Date: Thu, 26 Mar 2020 09:04:45 GMT
• Title: Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction
• Authors: Yan Xiao, Yaochu Jin, Ran Cheng, Kuangrong Hao
• Abstract summary: We propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. The proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset.
• Score: 20.644215991166902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As one basic task for natural language processing (NLP), relation extraction aims to extract the semantic relation between entity pairs based on the given text. To avoid manual labeling of datasets, distant supervision relation extraction (DSRE) has been widely used, aiming to utilize knowledge base to automatically annotate datasets. Unfortunately, this method heavily suffers from wrong labelling due to the underlying strong assumptions. To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. More specifically, the Transformer block is firstly used as the sentence encoder to capture syntactic information of sentences, which mainly utilizes multi-head self-attention to extract features from word level. Then, a more concise sentence-level attention mechanism is adopted to constitute the bag representation, aiming to incorporate valid information of each sentence to effectively represent the bag. Experimental results on the public dataset New York Times (NYT) demonstrate that the proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset, which verifies the effectiveness of our model for the DSRE task.

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