BioMRC: A Dataset for Biomedical Machine Reading Comprehension

By Petros Stavropoulos, Dimitris Pappas, Ion Androutsopoulos, Ryan T. McDonald. 2020

59 citations recorded by the source corpus.

We introduceBIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Noah A. Smith
2. Matt Gardner
3. Iz Beltagy
4. Arman Cohan
5. Hannaneh Hajishirzi
6. Kyle Lo
7. Jonathan Berant
8. Graham Neubig

In Molecular Biology

1. Kyle Lo
2. Arman Cohan
3. Iz Beltagy
4. Noah A. Smith
5. Matt Gardner
6. M. V. Koroteev
7. Daniel S. Weld
8. Heike Adel

Citations in the corpus

• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers (2021, background): “Some examples include emrQA (Pampari et al., 2018), BioRead (Pappas et al., 2018), BioMRC (Pappas et al., 2020), MedHop (Welbl et al., 2018).”