# NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations

arXiv: https://arxiv.org/abs/2109.10080 · HTML: https://mlsanity.com/paper/2109.10080/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies](https://mlsanity.com/paper/2105.08882/) (2021)
- [Resolving the Scope of Speculation and Negation using Transformer-Based Architectures](https://mlsanity.com/paper/2001.02885/) (2020)

## Built on by (most cited first, 5 of 5)

- [Generalizing over Long Tail Concepts for Medical Term Normalization](https://mlsanity.com/paper/2210.11947/) (2022)
- [Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction](https://mlsanity.com/paper/2306.05276/) (2023)
- [Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation](https://mlsanity.com/paper/2209.02812/) (2022)
- [Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective](https://mlsanity.com/paper/2208.02031/) (2022)
- [Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates](https://mlsanity.com/paper/2407.02432/) (2024)

## Abstract (arXiv)

Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models. We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models. Our dataset and code will be publicly released to encourage research on the topic.