UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Beat the ai: Investigating adversarial human annotation for reading comprehension

Bartolo, M; Roberts, A; Welbl, J; Riedel, S; Stenetorp, P; (2020) Beat the ai: Investigating adversarial human annotation for reading comprehension. Transactions of the Association for Computational Linguistics , 8 pp. 662-678. 10.1162/tacl_a_00338. Green open access

[thumbnail of tacl_a_00338.pdf]
Preview
Text
tacl_a_00338.pdf - Published version

Download (1MB) | Preview

Abstract

Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1 ).

Type: Article
Title: Beat the ai: Investigating adversarial human annotation for reading comprehension
Open access status: An open access version is available from UCL Discovery
DOI: 10.1162/tacl_a_00338
Publisher version: http://dx.doi.org/10.1162/tacl_a_00338
Language: English
Additional information: © 2020 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10131025
Downloads since deposit
10Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item