STARC: Structured Annotations for Reading Comprehension
Name
2020.acl-main.507.pdf
Description
Published version
Size
199.63 KB
Format
Adobe PDF
Checksum (MD5)
d5f8eaabd25f1c6661e43507414570a5
Author(s)
Berzak, Yevgeni
Malmaud, Jonathan
Levy, Roger
Date Issued
2020
Journal
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics (ACL)
Citation
Berzak, Yevgeni, Malmaud, Jonathan and Levy, Roger. 2020. "STARC: Structured Annotations for Reading Comprehension." Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
Version
Final published version
Abstract
We present STARC (Structured Annotations
for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our
framework introduces a principled structure
for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA, a new high-quality
dataset for evaluation and analysis of reading
comprehension in English. We use this dataset
to demonstrate that STARC can be leveraged
for a key new application for the development
of SAT-like reading comprehension materials:
automatic annotation quality probing via span
ablation experiments. We further show that
it enables in-depth analyses and comparisons
between machine and human reading comprehension behavior, including error distributions
and guessing ability. Our experiments also reveal that the standard multiple choice dataset
in NLP, RACE (Lai et al., 2017), is limited in
its ability to measure reading comprehension.
47% of its questions can be guessed by machines without accessing the passage, and 18%
are unanimously judged by humans as not having a unique correct answer. OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings
and has a substantially higher human ceiling
performance.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/V1/2020.ACL-MAIN.507
