# Stress Testing BERT Anaphora Resolution Models for Reaction Extraction in Chemical Patents

arXiv: https://arxiv.org/abs/2306.13379 · HTML: https://mlsanity.com/paper/2306.13379/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015)
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- [Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks](https://mlsanity.com/paper/2002.06261/) (2020)
- [Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings](https://mlsanity.com/paper/1907.02679/) (2019)
- [An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese](https://mlsanity.com/paper/1905.09642/) (2019)
- [Stress Test Evaluation of Biomedical Word Embeddings](https://mlsanity.com/paper/2107.11652/) (2021)
- [Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience](https://mlsanity.com/paper/2006.05281/) (2020)

## Abstract (arXiv)

The high volume of published chemical patents and the importance of a timely acquisition of their information gives rise to automating information extraction from chemical patents. Anaphora resolution is an important component of comprehensive information extraction, and is critical for extracting reactions. In chemical patents, there are five anaphoric relations of interest: co-reference, transformed, reaction associated, work up, and contained. Our goal is to investigate how the performance of anaphora resolution models for reaction texts in chemical patents differs in a noise-free and noisy environment and to what extent we can improve the robustness against noise of the model.