Papers with relation extractors

2 papers
Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction (D19-1)

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Challenge: Distantly Supervised Relation Extraction (DSRE) suffers from mislabelled data . human annotation on large datasets is costly and often impossible .
Approach: They propose a model that employs a collaborative curriculum learning framework to reduce mislabelled data.
Outcome: The proposed model outperforms baselines including state-of-the-art in terms of P@N and PR curve metrics on a widely-used public dataset.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)

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Challenge: Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process.
Approach: They propose a method to create prompts for a diverse set of tasks using a gradient-guided search.
Outcome: The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning.

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