Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension

Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension Max Bartolo Alastair Roberts Johannes Welbl Sebastian Riedel Pontus Stenetorp Department of Computer Science University College London {m.bartolo,a.roberts,j.welbl,s.riedel,p.stenetorp}@cs.ucl.ac.uk 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 samplesleads to strong generalization tonon-adversariallycollected 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 substantiallyweaker 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). 1 Introduction Data collection is a fundamental prerequisite for Machine Learning-based approaches to Natural Language Processing (NLP). Innovations in data acquisition methodology, such as crowdsourcing, have led to major breakthroughs in scalability and preceded the ‘‘deep learning revolution’’, for which they can arguably be seen as co-responsible (Deng et al., 2009; Bowman et al., 2015; Rajpurkar et al., 2016). Annotation approaches include ex- pert annotation, for example, relying on trained linguists (Marcus et al., 1993), crowd-sourcing by non-experts (Snow et al., 2008), distant supervi- sion (Mintz et al., 2009; Joshi et al., 2017), and leveraging document structure (Hermann et al., 2015). The concrete data collection paradigm cho- sen dictates the degree of scalability, annotation cost, precise task structure (often arising as a compromise of the above) and difficulty, domain coverage, as well as resulting dataset biases and model blind spots (Jia and Liang, 2017; Schwartz et al., 2017; Gururangan et al., 2018). A recently emerging trend in NLP dataset creation is the use of a model-in-the-loop when composing samples: A contemporary model is used either as a filter or directly during annotation, to identify samples wrongly predicted by the model. Examples of this method are realized in Build It Break It, The Language Edition (Ettinger et al., 2017), HotpotQA (Yang et al., 2018a), SWAG (Zellers et al., 2018), Mechanical Turker Descent (Yang et al., 2018b), DROP (Dua et al., 2019), CODAH (Chen et al., 2019), Quoref (Dasigi et al., 2019), and AdversarialNLI (Nie et al., 2019).1 This approach probes model robustness and ensures that the resulting datasets pose a challenge to current models, which drives research to tackle new sets of problems. We study this approach in the context of Reading Comprehension (RC), and investigate its robustness in the face of continuously progressing models—do …

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